import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import seaborn as sns

# Import data (accepted and rejected demands).
loan_data_accepted = pd.read_csv('C:/Disc D/365DataScience/Credit risk modeling/Self_project/Data2/accepted_2007_to_2018Q4.csv',low_memory=False)
loan_data_rejected = pd.read_csv('C:/Disc D/365DataScience/Credit risk modeling/Self_project/Data2/rejected_2007_to_2018Q4.csv',low_memory=False)

# Copy the dataframe.
loan_data_accepted = loan_data_accepted.copy()
loan_data_rejected = loan_data_rejected.copy()

# Expose the first 5 rows of the accepted loans.
pd.options.display.max_columns = None
loan_data_accepted.head()

# Expose the first 5 rows of the rejected loans.
loan_data_rejected.sample(5)

loan_data_accepted.shape

(2260701, 151)

loan_data = loan_data_accepted.copy()
pd.options.display.max_columns = None
#pd.options.display.max_rows = None
# Sets the pandas dataframe options to display all columns/ rows.

loan_data.columns.values
# Displays all column names.

array(['id', 'member_id', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv',
       'term', 'int_rate', 'installment', 'grade', 'sub_grade',
       'emp_title', 'emp_length', 'home_ownership', 'annual_inc',
       'verification_status', 'issue_d', 'loan_status', 'pymnt_plan',
       'url', 'desc', 'purpose', 'title', 'zip_code', 'addr_state', 'dti',
       'delinq_2yrs', 'earliest_cr_line', 'fico_range_low',
       'fico_range_high', 'inq_last_6mths', 'mths_since_last_delinq',
       'mths_since_last_record', 'open_acc', 'pub_rec', 'revol_bal',
       'revol_util', 'total_acc', 'initial_list_status', 'out_prncp',
       'out_prncp_inv', 'total_pymnt', 'total_pymnt_inv',
       'total_rec_prncp', 'total_rec_int', 'total_rec_late_fee',
       'recoveries', 'collection_recovery_fee', 'last_pymnt_d',
       'last_pymnt_amnt', 'next_pymnt_d', 'last_credit_pull_d',
       'last_fico_range_high', 'last_fico_range_low',
       'collections_12_mths_ex_med', 'mths_since_last_major_derog',
       'policy_code', 'application_type', 'annual_inc_joint', 'dti_joint',
       'verification_status_joint', 'acc_now_delinq', 'tot_coll_amt',
       'tot_cur_bal', 'open_acc_6m', 'open_act_il', 'open_il_12m',
       'open_il_24m', 'mths_since_rcnt_il', 'total_bal_il', 'il_util',
       'open_rv_12m', 'open_rv_24m', 'max_bal_bc', 'all_util',
       'total_rev_hi_lim', 'inq_fi', 'total_cu_tl', 'inq_last_12m',
       'acc_open_past_24mths', 'avg_cur_bal', 'bc_open_to_buy', 'bc_util',
       'chargeoff_within_12_mths', 'delinq_amnt', 'mo_sin_old_il_acct',
       'mo_sin_old_rev_tl_op', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl',
       'mort_acc', 'mths_since_recent_bc', 'mths_since_recent_bc_dlq',
       'mths_since_recent_inq', 'mths_since_recent_revol_delinq',
       'num_accts_ever_120_pd', 'num_actv_bc_tl', 'num_actv_rev_tl',
       'num_bc_sats', 'num_bc_tl', 'num_il_tl', 'num_op_rev_tl',
       'num_rev_accts', 'num_rev_tl_bal_gt_0', 'num_sats',
       'num_tl_120dpd_2m', 'num_tl_30dpd', 'num_tl_90g_dpd_24m',
       'num_tl_op_past_12m', 'pct_tl_nvr_dlq', 'percent_bc_gt_75',
       'pub_rec_bankruptcies', 'tax_liens', 'tot_hi_cred_lim',
       'total_bal_ex_mort', 'total_bc_limit',
       'total_il_high_credit_limit', 'revol_bal_joint',
       'sec_app_fico_range_low', 'sec_app_fico_range_high',
       'sec_app_earliest_cr_line', 'sec_app_inq_last_6mths',
       'sec_app_mort_acc', 'sec_app_open_acc', 'sec_app_revol_util',
       'sec_app_open_act_il', 'sec_app_num_rev_accts',
       'sec_app_chargeoff_within_12_mths',
       'sec_app_collections_12_mths_ex_med',
       'sec_app_mths_since_last_major_derog', 'hardship_flag',
       'hardship_type', 'hardship_reason', 'hardship_status',
       'deferral_term', 'hardship_amount', 'hardship_start_date',
       'hardship_end_date', 'payment_plan_start_date', 'hardship_length',
       'hardship_dpd', 'hardship_loan_status',
       'orig_projected_additional_accrued_interest',
       'hardship_payoff_balance_amount', 'hardship_last_payment_amount',
       'disbursement_method', 'debt_settlement_flag',
       'debt_settlement_flag_date', 'settlement_status',
       'settlement_date', 'settlement_amount', 'settlement_percentage',
       'settlement_term'], dtype=object)

loan_data.info()
# Displays column names, complete (non-missing) cases per column, and datatype per column.

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2260701 entries, 0 to 2260700
Columns: 151 entries, id to settlement_term
dtypes: float64(113), object(38)
memory usage: 2.5+ GB

loan_data['emp_length'].unique()
# Displays unique values of a column.

array(['10+ years', '3 years', '4 years', '6 years', '1 year', '7 years',
       '8 years', '5 years', '2 years', '9 years', '< 1 year', nan],
      dtype=object)

loan_data['emp_length_int'] = loan_data['emp_length'].str.replace('\\+ years', '', regex=True)
loan_data['emp_length_int'] = loan_data['emp_length_int'].str.replace('< 1 year', str(0))
loan_data['emp_length_int'] = loan_data['emp_length_int'].str.replace('n/a',  str(0))
loan_data['emp_length_int'] = loan_data['emp_length_int'].str.replace(' years', '')
loan_data['emp_length_int'] = loan_data['emp_length_int'].str.replace(' year', '')
# We store the preprocessed ‘employment length’ variable in a new variable called ‘employment length int’,
# We assign the new ‘employment length int’ to be equal to the ‘employment length’ variable with the string ‘+ years’
# replaced with nothing. Next, we replace the whole string ‘less than 1 year’ with the string ‘0’.
# Then, we replace the ‘n/a’ string with the string ‘0’. Then, we replace the string ‘space years’ with nothing.
# Finally, we replace the string ‘space year’ with nothing.

type(loan_data['emp_length_int'][0])
# Checks the datatype of a single element of a column.

str

loan_data['emp_length_int'].value_counts()

emp_length_int
10    748005
2     203677
0     189988
3     180753
1     148403
5     139698
4     136605
6     102628
7      92695
8      91914
9      79395
Name: count, dtype: int64

# Replace "Unknown" with NaN and then convert to numeric
loan_data['emp_length_int'] = loan_data['emp_length_int'].replace('Unknown', np.nan)

# Transforms the values to numeric.
loan_data['emp_length_int'] = pd.to_numeric(loan_data['emp_length_int'])

loan_data = loan_data.copy()

# Mode imputation
loan_data['emp_length_int'] = loan_data['emp_length_int'].fillna(loan_data['emp_length_int'].mode()[0])

loan_data['emp_length_int'].value_counts()

emp_length_int
10.0    894945
2.0     203677
0.0     189988
3.0     180753
1.0     148403
5.0     139698
4.0     136605
6.0     102628
7.0      92695
8.0      91914
9.0      79395
Name: count, dtype: int64

# Checking the percentage of the missing values for each category
loan_data['emp_length_int'].isnull().mean()

0.0

loan_data = loan_data.drop(columns = ['emp_length'])

loan_data['term'].describe()
# Shows some descriptive statisics for the values of a column.

count        2260668
unique             2
top        36 months
freq         1609754
Name: term, dtype: object

loan_data['term_int'] = loan_data['term'].str.replace(' months', '')
# We replace a string with another string, in this case, with an empty strng (i.e. with nothing).

loan_data['term_int'].sample(5)

1790274     60
1752897     36
1594219     36
175338      60
1387009     60
Name: term_int, dtype: object

type(loan_data['term_int'][25])
# Checks the datatype of a single element of a column.

str

loan_data['term_int'] = pd.to_numeric(loan_data['term'].str.replace(' months', ''))
# We remplace a string from a variable with another string, in this case, with an empty string (i.e. with nothing).
# We turn the result to numeric datatype and save it in another variable.
loan_data['term_int'].sample(5)

1682884    36.0
575675     36.0
1187779    36.0
821812     36.0
1579849    36.0
Name: term_int, dtype: float64

type(loan_data['term_int'][0])
# Checks the datatype of a single element of a column.

numpy.float64

loan_data['issue_d'].sample(5)

1662795    Mar-2017
1818823    Jul-2013
270902     May-2015
342562     Mar-2015
1418687    Nov-2018
Name: issue_d, dtype: object

# Assume we are now in December 2020
loan_data['issue_d_date'] = pd.to_datetime(loan_data['issue_d'], format='mixed', errors='coerce')
# Extracts the date and the time from a string variable that is in a given format.
loan_data['mths_since_issue_d'] = round(pd.to_numeric((pd.to_datetime('2020-12-01') - loan_data['issue_d_date']) / np.timedelta64(30, 'D')))
# We calculate the difference between two dates in months, turn it to numeric datatype and round it.
# We save the result in a new variable.
loan_data['mths_since_issue_d'].describe()
# Shows some descriptive statisics for the values of a column.

count    2.260668e+06
mean     5.581604e+01
std      2.189573e+01
min      2.400000e+01
25%      3.800000e+01
50%      5.400000e+01
75%      6.900000e+01
max      1.640000e+02
Name: mths_since_issue_d, dtype: float64

# Checking the percentage of the missing values for each category
missing_percent = loan_data.isnull().mean() * 100
missing_percent = missing_percent[missing_percent >= 0].sort_values(ascending=False)

# Set option to display all rows
pd.set_option('display.max_rows', None)

# Display the result
print(missing_percent)

member_id                                     100.000000
orig_projected_additional_accrued_interest     99.617331
hardship_loan_status                           99.517097
hardship_last_payment_amount                   99.517097
deferral_term                                  99.517097
hardship_status                                99.517097
hardship_reason                                99.517097
hardship_type                                  99.517097
hardship_end_date                              99.517097
payment_plan_start_date                        99.517097
hardship_length                                99.517097
hardship_dpd                                   99.517097
hardship_amount                                99.517097
hardship_payoff_balance_amount                 99.517097
hardship_start_date                            99.517097
settlement_date                                98.485160
debt_settlement_flag_date                      98.485160
settlement_term                                98.485160
settlement_status                              98.485160
settlement_percentage                          98.485160
settlement_amount                              98.485160
sec_app_mths_since_last_major_derog            98.410139
sec_app_revol_util                             95.303050
revol_bal_joint                                95.221836
sec_app_mort_acc                               95.221792
sec_app_fico_range_low                         95.221792
sec_app_chargeoff_within_12_mths               95.221792
sec_app_collections_12_mths_ex_med             95.221792
sec_app_inq_last_6mths                         95.221792
sec_app_num_rev_accts                          95.221792
sec_app_open_act_il                            95.221792
sec_app_open_acc                               95.221792
sec_app_earliest_cr_line                       95.221792
sec_app_fico_range_high                        95.221792
verification_status_joint                      94.880791
dti_joint                                      94.660683
annual_inc_joint                               94.660506
desc                                           94.423632
mths_since_last_record                         84.113069
mths_since_recent_bc_dlq                       77.011511
mths_since_last_major_derog                    74.309960
mths_since_recent_revol_delinq                 67.250910
next_pymnt_d                                   59.509993
mths_since_last_delinq                         51.246715
il_util                                        47.281042
mths_since_rcnt_il                             40.251099
all_util                                       38.323555
total_cu_tl                                    38.313912
open_acc_6m                                    38.313912
inq_last_12m                                   38.313912
open_rv_12m                                    38.313868
open_rv_24m                                    38.313868
total_bal_il                                   38.313868
max_bal_bc                                     38.313868
open_il_24m                                    38.313868
open_il_12m                                    38.313868
inq_fi                                         38.313868
open_act_il                                    38.313868
mths_since_recent_inq                          13.069751
emp_title                                       7.387178
num_tl_120dpd_2m                                6.798334
mo_sin_old_il_acct                              6.153136
bc_util                                         3.366389
percent_bc_gt_75                                3.335779
bc_open_to_buy                                  3.316140
mths_since_recent_bc                            3.248771
pct_tl_nvr_dlq                                  3.116909
avg_cur_bal                                     3.113149
mo_sin_rcnt_rev_tl_op                           3.110097
num_rev_accts                                   3.110097
mo_sin_old_rev_tl_op                            3.110097
num_actv_bc_tl                                  3.110053
mo_sin_rcnt_tl                                  3.110053
num_actv_rev_tl                                 3.110053
num_accts_ever_120_pd                           3.110053
total_il_high_credit_limit                      3.110053
num_il_tl                                       3.110053
num_bc_tl                                       3.110053
total_rev_hi_lim                                3.110053
tot_hi_cred_lim                                 3.110053
num_tl_op_past_12m                              3.110053
num_tl_90g_dpd_24m                              3.110053
num_tl_30dpd                                    3.110053
num_rev_tl_bal_gt_0                             3.110053
num_op_rev_tl                                   3.110053
tot_cur_bal                                     3.110053
tot_coll_amt                                    3.110053
num_bc_sats                                     2.593134
num_sats                                        2.593134
total_bc_limit                                  2.214490
total_bal_ex_mort                               2.214490
acc_open_past_24mths                            2.214490
mort_acc                                        2.214490
title                                           1.033264
last_pymnt_d                                    0.108816
revol_util                                      0.081170
dti                                             0.077144
pub_rec_bankruptcies                            0.061839
collections_12_mths_ex_med                      0.007874
chargeoff_within_12_mths                        0.007874
tax_liens                                       0.006104
last_credit_pull_d                              0.004645
inq_last_6mths                                  0.002787
earliest_cr_line                                0.002743
pub_rec                                         0.002743
open_acc                                        0.002743
delinq_2yrs                                     0.002743
acc_now_delinq                                  0.002743
delinq_amnt                                     0.002743
total_acc                                       0.002743
annual_inc                                      0.001637
zip_code                                        0.001504
disbursement_method                             0.001460
hardship_flag                                   0.001460
debt_settlement_flag                            0.001460
term_int                                        0.001460
issue_d_date                                    0.001460
mths_since_issue_d                              0.001460
application_type                                0.001460
sub_grade                                       0.001460
url                                             0.001460
pymnt_plan                                      0.001460
loan_status                                     0.001460
issue_d                                         0.001460
verification_status                             0.001460
home_ownership                                  0.001460
grade                                           0.001460
policy_code                                     0.001460
installment                                     0.001460
int_rate                                        0.001460
term                                            0.001460
funded_amnt_inv                                 0.001460
funded_amnt                                     0.001460
loan_amnt                                       0.001460
purpose                                         0.001460
addr_state                                      0.001460
fico_range_low                                  0.001460
fico_range_high                                 0.001460
last_fico_range_low                             0.001460
last_fico_range_high                            0.001460
last_pymnt_amnt                                 0.001460
collection_recovery_fee                         0.001460
recoveries                                      0.001460
total_rec_late_fee                              0.001460
total_rec_int                                   0.001460
total_rec_prncp                                 0.001460
total_pymnt_inv                                 0.001460
total_pymnt                                     0.001460
out_prncp_inv                                   0.001460
out_prncp                                       0.001460
initial_list_status                             0.001460
revol_bal                                       0.001460
emp_length_int                                  0.000000
id                                              0.000000
dtype: float64

features_to_drop = [
    'member_id', 'orig_projected_additional_accrued_interest',
    'hardship_end_date', 'deferral_term', 'hardship_status',
    'hardship_reason', 'hardship_type', 'hardship_payoff_balance_amount',
    'hardship_last_payment_amount', 'payment_plan_start_date',
    'hardship_amount', 'hardship_loan_status', 'hardship_start_date',
    'hardship_dpd', 'hardship_length', 'debt_settlement_flag_date',
    'settlement_date', 'settlement_amount', 'settlement_percentage',
    'settlement_term', 'settlement_status', 'sec_app_mths_since_last_major_derog',
    'sec_app_revol_util', 'revol_bal_joint', 'sec_app_inq_last_6mths',
    'sec_app_num_rev_accts', 'sec_app_open_act_il', 'sec_app_open_acc',
    'sec_app_mort_acc', 'sec_app_chargeoff_within_12_mths',
    'sec_app_collections_12_mths_ex_med', 'sec_app_fico_range_low',
    'sec_app_earliest_cr_line', 'sec_app_fico_range_high',
    'verification_status_joint', 'dti_joint', 'annual_inc_joint', 'desc'
]

# Drop the features with very high missing values (>90%)
loan_data = loan_data.drop(columns=features_to_drop)

# Step 1: Choose the relevant features and the target
delay_features = [
    'mths_since_last_record',
    'mths_since_recent_bc_dlq',
    'mths_since_last_major_derog'
]

# Example: Binary target variable preparation (adjust based on your dataset)
# Replace with appropriate mapping depending on your version of 'loan_status'
loan_data['target'] = loan_data['loan_status'].apply(lambda x: 1 if x in ['Charged Off', 'Default'] else 0)

# Step 2: Subset the data and drop rows with missing values in selected columns
df = loan_data[delay_features + ['target']].dropna()

# Step 3: Split into train/test
X = df[delay_features]
y = df['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Step 4: Train a Random Forest model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Step 5: Feature importance visualization
importances = model.feature_importances_
feat_importance = pd.Series(importances, index=delay_features).sort_values(ascending=True)

print(importances)
plt.figure(figsize=(8, 4))
sns.barplot(x=feat_importance, y=feat_importance.index, palette='viridis')
plt.title("Feature Importance (Random Forest)")
plt.xlabel("Importance Score")
plt.ylabel("Delay Feature")
plt.tight_layout()
plt.show()

[0.40604894 0.29841302 0.29553803]

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\2205266688.py:38: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(x=feat_importance, y=feat_importance.index, palette='viridis')

# List of the features
delay_features = ['mths_since_last_record', 'mths_since_recent_bc_dlq', 'mths_since_last_major_derog']

# Fill missing values with 999
loan_data[delay_features] = loan_data[delay_features].fillna(999)

# Fill 'months since' features with high value to indicate 'no delinquency'
loan_data['mths_since_recent_revol_delinq'] = loan_data['mths_since_recent_revol_delinq'].fillna(999)
loan_data['mths_since_last_delinq'] = loan_data['mths_since_last_delinq'].fillna(999)
loan_data['mths_since_rcnt_il'] = loan_data['mths_since_rcnt_il'].fillna(999)

# Date - next scheduled payment, could be missing because loan is fully paid off
loan_data['next_pymnt_d'] = loan_data['next_pymnt_d'].fillna('No Payment Due')  # or use pd.NaT if you prefer datetime

# Utilization ratios - fill with median or 0
loan_data['il_util'] = loan_data['il_util'].fillna(loan_data['il_util'].median())
loan_data['all_util'] = loan_data['all_util'].fillna(loan_data['all_util'].median())

# Counts / Frequency - fill with 0 (no activity)
count_cols = [
    'total_cu_tl', 'open_acc_6m', 'inq_last_12m', 'total_bal_il', 'max_bal_bc',
    'open_il_12m', 'open_act_il', 'inq_fi', 'open_rv_12m', 'open_rv_24m', 'open_il_24m'
]
loan_data[count_cols] = loan_data[count_cols].fillna(0)

loan_data = loan_data.drop(columns=['emp_title'])

# Months since events
loan_data['mths_since_recent_inq'] = loan_data['mths_since_recent_inq'].fillna(999)

# Utilization/ratio: median
ratio_cols = ['bc_util', 'percent_bc_gt_75', 'pct_tl_nvr_dlq']
loan_data[ratio_cols] = loan_data[ratio_cols].fillna(loan_data[ratio_cols].median())

# Timeline features: median
timeline_cols = ['mo_sin_old_il_acct', 'mo_sin_old_rev_tl_op', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl']
loan_data[timeline_cols] = loan_data[timeline_cols].fillna(loan_data[timeline_cols].median())

# Balances and limits: median
balance_cols = [
    'bc_open_to_buy', 'avg_cur_bal', 'total_rev_hi_lim', 'tot_cur_bal',
    'total_il_high_credit_limit', 'tot_hi_cred_lim', 'total_bc_limit', 'total_bal_ex_mort'
]
loan_data[balance_cols] = loan_data[balance_cols].fillna(loan_data[balance_cols].median())

# Count features: fill with 0
count_cols = [
    'num_rev_accts', 'num_accts_ever_120_pd', 'num_actv_bc_tl', 'num_actv_rev_tl',
    'num_rev_tl_bal_gt_0', 'num_tl_90g_dpd_24m', 'num_tl_30dpd', 'num_tl_op_past_12m',
    'num_op_rev_tl', 'num_il_tl', 'num_bc_tl', 'num_bc_sats', 'num_sats',
    'mort_acc', 'acc_open_past_24mths', 'tot_coll_amt', 'num_tl_120dpd_2m'
]
loan_data[count_cols] = loan_data[count_cols].fillna(0)

# Title (text field)
loan_data['title'] = loan_data['title'].fillna("Unknown")

# mths_since_recent_bc
loan_data['mths_since_recent_bc'] = loan_data['mths_since_recent_bc'].fillna(loan_data['mths_since_recent_bc'].median())

loan_data['earliest_cr_line_date'] = pd.to_datetime(loan_data['earliest_cr_line'], format='mixed', errors='coerce')
# Extracts the date and the time from a string variable that is in a given format.

type(loan_data['earliest_cr_line_date'][0])
# Checks the datatype of a single element of a column.

pandas._libs.tslibs.timestamps.Timestamp

# Assume we are now in December 2020
loan_data['mths_since_earliest_cr_line'] = round(pd.to_numeric((pd.to_datetime('2020-12-01') - loan_data['earliest_cr_line_date'])/np.timedelta64(30, 'D')))
# We calculate the difference between two dates in months, turn it to numeric datatype and round it.
# We save the result in a new variable.

loan_data['mths_since_earliest_cr_line'].describe()
# Shows some descriptive statisics for the values of a column.
# Dates from 1969 and before are not being converted well, i.e., they have become 2069 and similar,
# and negative differences are being calculated.

count    2.260701e+06
mean     2.553759e+02
std      9.554257e+01
min      6.200000e+01
25%      1.900000e+02
50%      2.390000e+02
75%      3.050000e+02
max      1.068000e+03
Name: mths_since_earliest_cr_line, dtype: float64

loan_data['earliest_cr_line_date'].sample(5)

405485    2000-01-01
1230975   1995-12-01
586343    2006-08-01
290012    2006-02-01
865494    1998-02-01
Name: earliest_cr_line_date, dtype: datetime64[ns]

# 1. Date columns - fill with most frequent date or a placeholder (e.g., 'Jan-2019')
date_cols = ['last_pymnt_d', 'last_credit_pull_d', 'earliest_cr_line']
for col in date_cols:
    most_common_date = loan_data[col].mode()[0]
    loan_data[col] = loan_data[col].fillna(most_common_date)

# 2. Ratios: Fill with median
loan_data['revol_util'] = loan_data['revol_util'].fillna(loan_data['revol_util'].median())
loan_data['dti'] = loan_data['dti'].fillna(loan_data['dti'].median())

# 3. Credit public records: Assume 0 means no record
zero_fill_cols = [
    'pub_rec_bankruptcies', 'chargeoff_within_12_mths', 'collections_12_mths_ex_med',
    'tax_liens', 'pub_rec', 'delinq_2yrs', 'delinq_amnt', 'acc_now_delinq'
]
loan_data[zero_fill_cols] = loan_data[zero_fill_cols].fillna(0)

# 4. Credit counts: fill with median
count_cols = [
    'open_acc', 'total_acc', 'inq_last_6mths', 'mths_since_earliest_cr_line'
]
loan_data[count_cols] = loan_data[count_cols].fillna(loan_data[count_cols].median())

# 5. Annual income
loan_data['annual_inc'] = loan_data['annual_inc'].fillna(loan_data['annual_inc'].median())

# 6. Zip code
loan_data['zip_code'] = loan_data['zip_code'].fillna(loan_data['zip_code'].mode()[0])

# 'last_credit_pull_d' and 'last_pymnt_d' are date features that give temporal insight into borrower behavior and loan servicing
# Creating time-based features like “months since last payment” or “months since last credit pull” can really help the credit risk model
# understand borrower behavior better. 

# Convert date columns to datetime if they aren't already
loan_data['last_pymnt_d'] = pd.to_datetime(loan_data['last_pymnt_d'], format='mixed', errors='coerce')
loan_data['last_credit_pull_d'] = pd.to_datetime(loan_data['last_credit_pull_d'], format='mixed', errors='coerce')

# Reference date — can use today or a fixed date (e.g., end of data collection period)
reference_date = pd.to_datetime("2020-12-31")  # Replace with appropriate date based on your dataset

# Create new features: months since last payment and last credit pull
loan_data['months_since_last_pymnt'] = round(pd.to_numeric((reference_date - loan_data['last_pymnt_d']) / np.timedelta64(30, 'D')))
loan_data['months_since_last_credit_pull'] = round(pd.to_numeric((reference_date - loan_data['last_credit_pull_d']) / np.timedelta64(30, 'D')))

# Categorical columns to fill with mode
cat_cols = [
    'disbursement_method', 'hardship_flag', 'pymnt_plan', 'application_type', 'verification_status',
    'home_ownership', 'initial_list_status', 'term', 'purpose', 'addr_state',
    'sub_grade', 'grade', 'policy_code', 'debt_settlement_flag'
]
for col in cat_cols:
    loan_data[col] = loan_data[col].fillna(loan_data[col].mode()[0])

# Numeric columns to fill with median
num_cols = [
    'int_rate', 'installment', 'term_int', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv',
    'fico_range_low', 'fico_range_high', 'last_fico_range_low', 'last_fico_range_high',
    'last_pymnt_amnt', 'collection_recovery_fee', 'recoveries', 'total_rec_late_fee',
    'total_rec_int', 'total_rec_prncp', 'total_pymnt_inv', 'total_pymnt', 'out_prncp_inv',
    'out_prncp', 'revol_bal'
]
loan_data[num_cols] = loan_data[num_cols].fillna(loan_data[num_cols].median())

# Date columns - fill with mode
date_cols = ['issue_d', 'issue_d_date', 'mths_since_issue_d']
for col in date_cols:
    loan_data[col] = loan_data[col].fillna(loan_data[col].mode()[0])

# Drop 'url' if not used
loan_data = loan_data.drop(columns=['url'])

# Checking the percentage of the missing values for each category
missing_percent = loan_data.isnull().mean() * 100
missing_percent = missing_percent[missing_percent > 0].sort_values(ascending=False)

# Display the result
print(missing_percent)

loan_status    0.00146
dtype: float64

# Display a sample of 5 entries.
loan_data.sample(5)

# Drop the column 'target'
loan_data = loan_data.drop(columns=['target'])

loan_data.shape

(2260701, 118)

loan_data['loan_status'].unique()
# Displays unique values of 'loan_status' column.

array(['Fully Paid', 'Current', 'Charged Off', 'In Grace Period',
       'Late (31-120 days)', 'Late (16-30 days)', 'Default', nan,
       'Does not meet the credit policy. Status:Fully Paid',
       'Does not meet the credit policy. Status:Charged Off'],
      dtype=object)

loan_data['loan_status'].value_counts()
# Calculates the number of observations for each unique value of a variable.

loan_status
Fully Paid                                             1076751
Current                                                 878317
Charged Off                                             268559
Late (31-120 days)                                       21467
In Grace Period                                           8436
Late (16-30 days)                                         4349
Does not meet the credit policy. Status:Fully Paid        1988
Does not meet the credit policy. Status:Charged Off        761
Default                                                     40
Name: count, dtype: int64

loan_data['loan_status'].value_counts() / loan_data['loan_status'].count() *100
# We divide the number of observations for each unique value of a variable by the total number of observations.
# Thus, we get the proportion of observations for each unique value of a variable.

loan_status
Fully Paid                                             47.629771
Current                                                38.852100
Charged Off                                            11.879630
Late (31-120 days)                                      0.949587
In Grace Period                                         0.373164
Late (16-30 days)                                       0.192377
Does not meet the credit policy. Status:Fully Paid      0.087939
Does not meet the credit policy. Status:Charged Off     0.033663
Default                                                 0.001769
Name: count, dtype: float64

# Step 1: Keep only loans with known outcomes
loan_data_clean = loan_data[loan_data['loan_status'].isin(['Fully Paid','Charged Off','Default',
                                                           'Late (31-120 days)','Late (16-30 days)'])]

# copy the datafile
loan_data_clean = loan_data_clean.copy()

# Good/ Bad Definition
loan_data_clean['good_bad'] = np.where(
    loan_data_clean['loan_status'].isin(['Charged Off', 'Default', 
                                         'Late (31-120 days)', 'Late (16-30 days)']), 1, 0)
# We create a new variable that has the value of '0' if a condition is met, and the value of '1' if it is not met.

# shape of the final cleaned dataset
loan_data_clean.shape

(1371166, 119)

from sklearn.model_selection import train_test_split
# Imports the libraries we need.

loan_data_inputs_train, loan_data_inputs_test, loan_data_targets_train, loan_data_targets_test = train_test_split(
              loan_data_clean.drop('good_bad', axis = 1), loan_data_clean['good_bad'], test_size = 0.2, random_state = 42)
# We split two dataframes with inputs and targets, each into a train and test dataframe, and store them in variables.
# This time we set the size of the test dataset to be 20%.
# Respectively, the size of the train dataset becomes 80%.
# We also set a specific random state.
# This would allow us to perform the exact same split multimple times.
# This means, to assign the exact same observations to the train and test datasets.

loan_data_inputs_train.shape
# Displays the size of the dataframe.

(1096932, 118)

loan_data_targets_train.shape
# Displays the size of the dataframe.

(1096932,)

loan_data_inputs_test.shape
# Displays the size of the dataframe.

(274234, 118)

loan_data_targets_test.shape
# Displays the size of the dataframe.

(274234,)

#####
#df_inputs_prepr = loan_data_inputs_train
#df_targets_prepr = loan_data_targets_train
#####
df_inputs_prepr = loan_data_inputs_test
df_targets_prepr = loan_data_targets_test

# WoE function for discrete unordered variables
def woe_discrete(df, discrete_variabe_name, good_bad_variable_df):

    # Concatenates two dataframes along the columns.
    df = pd.concat([df[discrete_variabe_name], good_bad_variable_df], axis = 1)

    # Groups the data according to a criterion contained in one column.
    # Does not turn the names of the values of the criterion as indexes.
    # Aggregates the data in another column, using a selected function.
    # In this specific case, we group by the column with index 0 and we aggregate the values of the column with index 1.
    # More specifically, we count them.
    # In other words, we count the values in the column with index 1 for each value of the column with index 0.
    # We calculate then the mean of the values in the column with index 1 for each value of the column with index 0.
    # And concatenate two dataframes along the columns.
    df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
                    df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)
   
    # Selects only columns with specific indexes.
    df = df.iloc[:, [0, 1, 3]]

    # Changes the names of the columns of a dataframe.
    df.columns = [df.columns.values[0], 'n_obs', 'prop_good']

    # We divide the values of one column by he values of another column and save the result in a new variable.
    df['prop_n_obs'] = df['n_obs'] / df['n_obs'].sum()

    # We multiply the values of one column by he values of another column and save the result in a new variable.
    df['n_good'] = df['prop_good'] * df['n_obs']
    df['n_bad'] = (1 - df['prop_good']) * df['n_obs']

    # We calculate the proportion of good and the proportion of bad observations
    df['prop_n_good'] = df['n_good'] / df['n_good'].sum()
    df['prop_n_bad'] = df['n_bad'] / df['n_bad'].sum()

    # We take the natural logarithm of a variable and save the result in a nex variable.
    # WoE = Weight of Evidence
    df['WoE'] = np.log(df['prop_n_good'] / df['prop_n_bad'])
    
    # Sorts a dataframe by the values of a given column.
    df = df.sort_values(['WoE'])

    # We reset the index of a dataframe and overwrite it.
    df = df.reset_index(drop = True)

    # We take the difference between two subsequent values of a column. Then, we take the absolute value of the result.
    df['diff_prop_good'] = df['prop_good'].diff().abs()

    # We take the difference between two subsequent values of a column. Then, we take the absolute value of the result.
    df['diff_WoE'] = df['WoE'].diff().abs()

    # We sum all values of a given column.
    df['IV'] = (df['prop_n_good'] - df['prop_n_bad']) * df['WoE']
    df['IV'] = df['IV'].sum()
    return df
# The function takes 3 arguments: a dataframe, a string, and a dataframe. The function returns a dataframe as a result.

# Imports the libraries we need.
sns.set()
# We set the default style of the graphs to the seaborn style.

# Below we define a function that takes 2 arguments: a dataframe and a number.
# The number parameter has a default value of 0.
# This means that if we call the function and omit the number parameter, it will be executed with it having a value of 0.
# The function displays a graph.
def plot_by_woe(df_WoE, rotation_of_x_axis_labels = 0):
    x = np.array(df_WoE.iloc[:, 0].apply(str))
    # Turns the values of the column with index 0 to strings, makes an array from these strings, and passes it to variable x.
    y = df_WoE['WoE']
    # Selects a column with label 'WoE' and passes it to variable y.
    plt.figure(figsize=(18, 6))
    # Sets the graph size to width 18 x height 6.
    plt.plot(x, y, marker = 'o', linestyle = '--', color = 'k')
    # Plots the datapoints with coordiantes variable x on the x-axis and variable y on the y-axis.
    # Sets the marker for each datapoint to a circle, the style line between the points to dashed, and the color to black.
    plt.xlabel(df_WoE.columns[0])
    # Names the x-axis with the name of the column with index 0.
    plt.ylabel('Weight of Evidence')
    # Names the y-axis 'Weight of Evidence'.
    plt.title(str('Weight of Evidence by ' + df_WoE.columns[0]))
    # Names the grapth 'Weight of Evidence by ' the name of the column with index 0.
    plt.xticks(rotation = rotation_of_x_axis_labels)
    # Rotates the labels of the x-axis a predefined number of degrees.

# Check the list of the categorical features of the dataset
categorical_vars = loan_data_clean.select_dtypes(include=['object', 'category']).columns.tolist()
print(categorical_vars)
print()
print('Number of categorical variables :   ',len(categorical_vars))

['id', 'term', 'grade', 'sub_grade', 'home_ownership', 'verification_status', 'issue_d', 'loan_status', 'pymnt_plan', 'purpose', 'title', 'zip_code', 'addr_state', 'earliest_cr_line', 'initial_list_status', 'next_pymnt_d', 'application_type', 'hardship_flag', 'disbursement_method', 'debt_settlement_flag']

Number of categorical variables :    20

list_col_to_drop = ['id', 'title', 'zip_code', 'earliest_cr_line', 'earliest_cr_line_date', 'next_pymnt_d', 'issue_d',
                    'issue_d_date', 'pymnt_plan',  'term', 'last_pymnt_d', 'last_credit_pull_d', 'loan_status']

df_inputs_prepr = df_inputs_prepr.drop(columns = list_col_to_drop)

df_inputs_prepr.shape

(274234, 105)

# Check the list of the categorical features of the dataset
categorical_vars_final = df_inputs_prepr.select_dtypes(include=['object', 'category']).columns.tolist()
print(categorical_vars_final)

['grade', 'sub_grade', 'home_ownership', 'verification_status', 'purpose', 'addr_state', 'initial_list_status', 'application_type', 'hardship_flag', 'disbursement_method', 'debt_settlement_flag']

# Unique values of 'grade' feature
df_inputs_prepr['grade'].unique()

array(['C', 'D', 'A', 'E', 'F', 'B', 'G'], dtype=object)

# Unique values of 'sub_grade' feature
df_inputs_prepr['sub_grade'].unique()

array(['C4', 'D2', 'C1', 'A5', 'E3', 'C3', 'C5', 'F4', 'E2', 'B2', 'A1',
       'C2', 'D3', 'B1', 'B4', 'E5', 'B5', 'D1', 'B3', 'A4', 'A3', 'E1',
       'D5', 'F5', 'F2', 'G4', 'A2', 'D4', 'F1', 'G1', 'G5', 'G2', 'G3',
       'F3', 'E4'], dtype=object)

# Drop the 'sub_grade' column.
df_inputs_prepr = df_inputs_prepr.drop(columns = ['sub_grade'])

# 'grade'
df_temp = woe_discrete(df_inputs_prepr, 'grade', df_targets_prepr)
# We execute the function we defined with the necessary arguments: a dataframe, a string, and a dataframe.
# We store the result in a dataframe.
df_temp

plot_by_woe(df_temp)
# We execute the function we defined with the necessary arguments: a dataframe.
# We omit the number argument, which means the function will use its default value, 0.

df_var_dummies =[pd.get_dummies(df_inputs_prepr['grade'], prefix = 'grade', prefix_sep = ':')]
# We create dummy variables from original independent variables, and save them into a list.
# Note that we are using a particular naming convention for all variables: original variable name, colon, category name.

df_var_dummies = pd.concat(df_var_dummies, axis = 1)
# We concatenate the dummy variables and this turns them into a dataframe.

df_inputs_prepr = pd.concat([df_inputs_prepr, df_var_dummies], axis = 1)
# Concatenates two dataframes.
# Here we concatenate the dataframe with original data with the dataframe with dummy variables, along the columns.

df_inputs_prepr.head()

# 'home_ownership'
df_temp = woe_discrete(df_inputs_prepr, 'home_ownership', df_targets_prepr)
# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp)
# We plot the weight of evidence values.

df_var_dummies = [pd.get_dummies(df_inputs_prepr['home_ownership'], prefix = 'home_ownership', prefix_sep = ':')]
# We create dummy variables from original independent variables, and save them into a list.
# Note that we are using a particular naming convention for all variables: original variable name, colon, category name.

df_var_dummies = pd.concat(df_var_dummies, axis = 1)
# We concatenate the dummy variables and this turns them into a dataframe.

# There are many categories with very few observations and many categories with very different "good" %.
# Therefore, we create a new discrete variable where we combine some of the categories.
# 'OTHERS' and 'NONE' are riskiest but are very few. 'RENT' is the next riskiest.
# 'ANY' are least risky but are too few. Conceptually, they belong to the same category. Also, their inclusion would not change anything.
# We combine them in one category, 'RENT_OTHER_NONE_ANY'.
# We end up with 3 categories: 'RENT_OTHER_NONE_ANY', 'OWN', 'MORTGAGE'.
df_var_dummies['home_ownership:RENT_OTHER_NONE_ANY'] = sum([df_var_dummies['home_ownership:RENT'], df_var_dummies['home_ownership:OTHER'],
                                                            df_var_dummies['home_ownership:NONE'], df_var_dummies['home_ownership:ANY']])
# 'RENT_OTHER_NONE_ANY' will be the reference category.

df_var_dummies = df_var_dummies.drop(columns = ['home_ownership:RENT', 'home_ownership:OTHER', 'home_ownership:NONE', 'home_ownership:ANY'])
# Drop the dummy variables that are grouped together 

df_inputs_prepr = pd.concat([df_inputs_prepr, df_var_dummies], axis = 1)
# Concatenates two dataframes.
# Here we concatenate the dataframe with original data with the dataframe with dummy variables, along the columns.

# 'addr_state'
df_inputs_prepr['addr_state'].unique()

array(['SD', 'CA', 'GA', 'NV', 'NY', 'PA', 'MD', 'MT', 'FL', 'CO', 'NJ',
       'CT', 'TX', 'WV', 'MA', 'NM', 'NC', 'IL', 'AZ', 'IN', 'MO', 'HI',
       'NE', 'NH', 'WA', 'KY', 'SC', 'TN', 'MN', 'LA', 'RI', 'VA', 'UT',
       'AL', 'ND', 'OH', 'MI', 'ID', 'KS', 'DE', 'OR', 'WY', 'AK', 'WI',
       'ME', 'AR', 'MS', 'OK', 'VT', 'DC', 'IA'], dtype=object)

df_temp = woe_discrete(df_inputs_prepr, 'addr_state', df_targets_prepr)
# We calculate weight of evidence.
df_temp

C:\Users\pc\anaconda3\envs\envname\Lib\site-packages\pandas\core\arraylike.py:399: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)

plot_by_woe(df_temp)
# We plot the weight of evidence values.

plot_by_woe(df_temp.iloc[2: -2, : ])
# We plot the weight of evidence values.

plot_by_woe(df_temp.iloc[6: -6, : ])
# We plot the weight of evidence values.

df_var_dummies = [pd.get_dummies(df_inputs_prepr['addr_state'], prefix = 'addr_state', prefix_sep = ':')]
# We create dummy variables from original independent variables, and save them into a list.
# Note that we are using a particular naming convention for all variables: original variable name, colon, category name.

df_var_dummies = pd.concat(df_var_dummies, axis = 1)
# We concatenate the dummy variables and this turns them into a dataframe.

# We create the following categories:
# 'ND' 'NE' 'IA' NV' 'FL' 'HI' 'AL'
# 'NM' 'VA'
# 'NY'
# 'OK' 'TN' 'MO' 'LA' 'MD' 'NC'
# 'CA'
# 'UT' 'KY' 'AZ' 'NJ'
# 'AR' 'MI' 'PA' 'OH' 'MN'
# 'RI' 'MA' 'DE' 'SD' 'IN'
# 'GA' 'WA' 'OR'
# 'WI' 'MT'
# 'TX'
# 'IL' 'CT'
# 'KS' 'SC' 'CO' 'VT' 'AK' 'MS'
# 'WV' 'NH' 'WY' 'DC' 'ME' 'ID'

# 'IA_NV_HI_ID_AL_FL' will be the reference category.

df_inputs_prepr['addr_state:ND_NE_IA_NV_FL_HI_AL'] = sum([df_var_dummies['addr_state:ND'], df_var_dummies['addr_state:NE'],
                                              df_var_dummies['addr_state:IA'], df_var_dummies['addr_state:NV'],
                                              df_var_dummies['addr_state:FL'], df_var_dummies['addr_state:HI'],
                                                          df_var_dummies['addr_state:AL']])

df_inputs_prepr['addr_state:NM_VA'] = sum([df_var_dummies['addr_state:NM'], df_var_dummies['addr_state:VA']])

df_inputs_prepr['addr_state:OK_TN_MO_LA_MD_NC'] = sum([df_var_dummies['addr_state:OK'], df_var_dummies['addr_state:TN'],
                                                       df_var_dummies['addr_state:MO'], df_var_dummies['addr_state:LA'],
                                                       df_var_dummies['addr_state:MD'], df_var_dummies['addr_state:NC']])

df_inputs_prepr['addr_state:UT_KY_AZ_NJ'] = sum([df_var_dummies['addr_state:UT'], df_var_dummies['addr_state:KY'],
                                                 df_var_dummies['addr_state:AZ'], df_var_dummies['addr_state:NJ']])

df_inputs_prepr['addr_state:AR_MI_PA_OH_MN'] = sum([df_var_dummies['addr_state:AR'], df_var_dummies['addr_state:MI'],
                                                    df_var_dummies['addr_state:PA'], df_var_dummies['addr_state:OH'],
                                                    df_var_dummies['addr_state:MN']])

df_inputs_prepr['addr_state:RI_MA_DE_SD_IN'] = sum([df_var_dummies['addr_state:RI'], df_var_dummies['addr_state:MA'],
                                                    df_var_dummies['addr_state:DE'], df_var_dummies['addr_state:SD'],
                                                    df_var_dummies['addr_state:IN']])

df_inputs_prepr['addr_state:GA_WA_OR'] = sum([df_var_dummies['addr_state:GA'], df_var_dummies['addr_state:WA'],
                                              df_var_dummies['addr_state:OR']])

df_inputs_prepr['addr_state:WI_MT'] = sum([df_var_dummies['addr_state:WI'], df_var_dummies['addr_state:MT']])

df_inputs_prepr['addr_state:IL_CT'] = sum([df_var_dummies['addr_state:IL'], df_var_dummies['addr_state:CT']])

df_inputs_prepr['addr_state:KS_SC_CO_VT_AK_MS'] = sum([df_var_dummies['addr_state:KS'], df_var_dummies['addr_state:SC'],
                                                       df_var_dummies['addr_state:CO'], df_var_dummies['addr_state:VT'],
                                                       df_var_dummies['addr_state:AK'], df_var_dummies['addr_state:MS']])

df_inputs_prepr['addr_state:WV_NH_WY_DC_ME_ID'] = sum([df_var_dummies['addr_state:WV'], df_var_dummies['addr_state:NH'],
                                                       df_var_dummies['addr_state:WY'], df_var_dummies['addr_state:DC'],
                                                       df_var_dummies['addr_state:ME'], df_var_dummies['addr_state:ID']])

# 'verification_status'
df_temp = woe_discrete(df_inputs_prepr, 'verification_status', df_targets_prepr)
# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp)
# We plot the weight of evidence values.

df_var_dummies = [pd.get_dummies(df_inputs_prepr['verification_status'], prefix = 'verification_status', prefix_sep = ':')]
# We create dummy variables from original independent variables, and save them into a list.
# Note that we are using a particular naming convention for all variables: original variable name, colon, category name.

df_var_dummies = pd.concat(df_var_dummies, axis = 1)
# We concatenate the dummy variables and this turns them into a dataframe.

df_inputs_prepr = pd.concat([df_inputs_prepr, df_var_dummies], axis = 1)
# Concatenates two dataframes.
# Here we concatenate the dataframe with original data with the dataframe with dummy variables, along the columns.

# 'purpose'
df_temp = woe_discrete(df_inputs_prepr, 'purpose', df_targets_prepr)
# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

df_var_dummies = [pd.get_dummies(df_inputs_prepr['purpose'], prefix = 'purpose', prefix_sep = ':')]
# We create dummy variables from original independent variables, and save them into a list.
# Note that we are using a particular naming convention for all variables: original variable name, colon, category name.

df_var_dummies = pd.concat(df_var_dummies, axis = 1)
# We concatenate the dummy variables and this turns them into a dataframe.

# We combine 'small_business', 'moving', 'renewable_energy', 'house' and 'medical' in one category: 'sm_b__mov__ren_en__house__medic'.
# We combine 'other', 'vacation' and 'major_purchase' in one category: other__vacat__maj_purch'.
# We combine 'home_improvement', ''wedding'educational', 'car' and  in one category: 'home_impr__educ__car__wed'.
# We leave 'debt_consolidtion' in a separate category.
# We leave 'credit_card' in a separate category.
#'sm_b__mov__ren_en__house__medic' will be the reference category.
df_inputs_prepr['purpose:debt_consolidation'] = df_var_dummies['purpose:debt_consolidation']
df_inputs_prepr['purpose:credit_card'] = df_var_dummies['purpose:credit_card']
df_inputs_prepr['purpose:sm_b__mov__ren_en__house__medic'] = sum([df_var_dummies['purpose:small_business'], df_var_dummies['purpose:moving'],
                                                                  df_var_dummies['purpose:renewable_energy'],df_var_dummies['purpose:house'],
                                                                  df_var_dummies['purpose:medical']])
df_inputs_prepr['purpose:other__vacat__maj_purch'] = sum([df_var_dummies['purpose:other'], df_var_dummies['purpose:major_purchase'],
                                                          df_var_dummies['purpose:vacation']])
df_inputs_prepr['purpose:home_impr__educ__car__wed'] = sum([df_var_dummies['purpose:home_improvement'], df_var_dummies['purpose:car'],
                                                              df_var_dummies['purpose:home_improvement'],  df_var_dummies['purpose:wedding']])

loan_data_dummies = [pd.get_dummies(df_inputs_prepr['initial_list_status'], prefix = 'initial_list_status', prefix_sep = ':'),
                     pd.get_dummies(df_inputs_prepr['application_type'], prefix = 'application_type', prefix_sep = ':'),
                     pd.get_dummies(df_inputs_prepr['hardship_flag'], prefix = 'hardship_flag', prefix_sep = ':'),
                     pd.get_dummies(df_inputs_prepr['disbursement_method'], prefix = 'disbursement_method', prefix_sep = ':'),
                     pd.get_dummies(df_inputs_prepr['debt_settlement_flag'], prefix = 'debt_settlement_flag', prefix_sep = ':')]
# We create dummy variables from all these original independent variables, and save them into a list.
# Note that we are using a particular naming convention for all variables: original variable name, colon, category name.

loan_data_dummies = pd.concat(loan_data_dummies, axis = 1)
# We concatenate the dummy variables and this turns them into a dataframe.

df_inputs_prepr = pd.concat([df_inputs_prepr, loan_data_dummies], axis = 1)
# Concatenates two dataframes.
# Here we concatenate the dataframe with original data with the dataframe with dummy variables, along the columns.

df_inputs_prepr = df_inputs_prepr.replace({True: 1, False: 0})

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\2025257523.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  df_inputs_prepr = df_inputs_prepr.replace({True: 1, False: 0})

df_inputs_prepr.head()

# Step 1: Get numerical columns
num_cols = loan_data_inputs_train.select_dtypes(include=['float64', 'int64']).columns

# Step 2: Filter continuous features (optional rule of thumb: more than 10 unique values)
continuous_features = [col for col in num_cols if loan_data_inputs_train[col].nunique() > 10]

print("Continuous features:")
print(continuous_features)
print()
print('Number of continuous features:   ', len(continuous_features))

Continuous features:
['min_mths_since_delinquency']

Number of continuous features:    1

# Get numerical features wit less than or equal 10 unique values (that can be considered as discrete features)
Other_features = [col for col in num_cols if loan_data_inputs_train[col].nunique() <= 10]

print("Numerical discrete features:")
print(Other_features)
print()
print('Number of numerical discrete features:   ', len(Other_features))

Numerical discrete features:
['grade:A', 'grade:B', 'grade:C', 'grade:D', 'grade:E', 'grade:F', 'grade:G', 'home_ownership:MORTGAGE', 'home_ownership:OWN', 'verification_status:Not Verified', 'verification_status:Source Verified', 'verification_status:Verified', 'purpose:debt_consolidation', 'purpose:credit_card', 'initial_list_status:f', 'initial_list_status:w', 'application_type:Individual', 'application_type:Joint App', 'hardship_flag:N', 'hardship_flag:Y', 'disbursement_method:Cash', 'disbursement_method:DirectPay', 'debt_settlement_flag:N', 'debt_settlement_flag:Y']

Number of numerical discrete features:    24

from statsmodels.stats.outliers_influence import variance_inflation_factor

def calculate_vif(data, num_features, sample_size=10000, random_state=42):
    """
    Calculates the Variance Inflation Factor (VIF) for the specified numerical features.

    Parameters:
    - data (pd.DataFrame): The full DataFrame.
    - num_features (list): List of numerical features to include in the VIF calculation.
    - sample_size (int): Number of rows to sample for computation.
    - random_state (int): Seed for reproducibility.

    Returns:
    - pd.DataFrame: Sorted DataFrame with features and their VIF values.
    """
    # Drop rows with missing values in the selected features
    X = data[num_features].dropna()

    # Sample the data to speed up VIF calculation
    X_sample = X.sample(n=sample_size, random_state=random_state)

    # Calculate VIF for each feature
    vif_data = pd.DataFrame()
    vif_data["feature"] = X_sample.columns
    vif_data["VIF"] = [variance_inflation_factor(X_sample.values, i) for i in range(X_sample.shape[1])]

    # Sort by VIF in descending order
    vif_data = vif_data.sort_values(by='VIF', ascending=False).reset_index(drop=True)
    
    return vif_data

def drop_highly_correlated_features(data, threshold=0.95):
    """
    Drops one of each pair of features with absolute correlation higher than the threshold.

    Parameters:
    - data (pd.DataFrame): Input DataFrame with numerical features.
    - threshold (float): Correlation threshold for dropping features.

    Returns:
    - pd.DataFrame: DataFrame with reduced features.
    - list: List of dropped features.
    """
    # Compute absolute correlation matrix
    corr_matrix = data.corr().abs()

    # Take the upper triangle of the correlation matrix
    upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))

    # Find columns with any correlation higher than the threshold
    to_drop = [col for col in upper.columns if any(upper[col] > threshold)]

    # Drop those columns
    reduced_data = data.drop(columns=to_drop)

    return reduced_data, to_drop

# List of the Loan Information features
num_features = ['loan_amnt', 'funded_amnt', 'funded_amnt_inv', 'term_int', 'int_rate', 'installment']

# Calculate and print Variance Inflation Factor (VIF)
print(calculate_vif(df_inputs_prepr, num_features))

           feature           VIF
0      funded_amnt  10171.338802
1  funded_amnt_inv   5723.614303
2        loan_amnt   3901.089311
3      installment     68.475463
4         term_int     21.619893
5         int_rate     15.790025

# List of the Loan Information features
num_features = ['loan_amnt', 'term_int', 'int_rate']

# Calculate and print Variance Inflation Factor (VIF)
print(calculate_vif(df_inputs_prepr, num_features))

     feature        VIF
0   term_int  13.595521
1   int_rate  10.039828
2  loan_amnt   4.353243

# List of the Loan Information features
num_features = ['annual_inc', 'emp_length_int', 'dti']

# Calculate and print Variance Inflation Factor (VIF)
print(calculate_vif(df_inputs_prepr, num_features))

          feature       VIF
0  emp_length_int  2.135642
1             dti  1.909319
2      annual_inc  1.369659

# List of the Loan Information features
num_features = ['delinq_2yrs', 'mths_since_last_delinq', 'mths_since_last_record', 'collections_12_mths_ex_med',
          'mths_since_last_major_derog', 'chargeoff_within_12_mths', 'delinq_amnt', 'acc_now_delinq', 'num_accts_ever_120_pd',
          'num_tl_90g_dpd_24m', 'num_tl_120dpd_2m', 'num_tl_30dpd']

# Calculate and print Variance Inflation Factor (VIF)
print(calculate_vif(df_inputs_prepr, num_features))

                        feature       VIF
0                acc_now_delinq  6.438133
1   mths_since_last_major_derog  6.050945
2                  num_tl_30dpd  5.342100
3        mths_since_last_record  4.121314
4        mths_since_last_delinq  3.568357
5              num_tl_120dpd_2m  2.516389
6                   delinq_2yrs  2.242248
7            num_tl_90g_dpd_24m  2.021835
8         num_accts_ever_120_pd  1.450038
9                   delinq_amnt  1.414542
10     chargeoff_within_12_mths  1.059052
11   collections_12_mths_ex_med  1.020881

# If multiple time-based features overlap, consider taking the minimum or latest date:
# Taking the minimum of 'mths_since_last_delinq' and 'mths_since_last_major_derog'
df_inputs_prepr['min_mths_since_delinquency'] = df_inputs_prepr[['mths_since_last_delinq', 'mths_since_last_major_derog']].min(axis=1)

# 'acc_now_delinq' shows if the borrower is currently delinquent on any account.
# 'mths_since_last_record' indicates how recently a public derogatory record (e.g., bankruptcy, judgment) was filed.
# Recommended Approach: Feature Combination with Risk Buckets

# Create a flag for current delinquency:
df_inputs_prepr['has_delinquency_now'] = (df_inputs_prepr['acc_now_delinq'] > 0).astype(int)

# Bucketize mths_since_last_record
df_inputs_prepr['last_record_bucket'] = pd.cut(df_inputs_prepr['mths_since_last_record'], bins=[-1, 12, 24, 60, np.inf], 
                                               labels=['<1yr', '1-2yr', '2-5yr', '5+yr'])

#  Combine into a single categorical feature
df_inputs_prepr['delinq_record_combo'] = df_inputs_prepr['has_delinquency_now'].astype(str) + '_' + df_inputs_prepr['last_record_bucket'].astype(str)

# Define risk levels manually = Transform 'delinq_record_combo' feature to numirical
risk_map = {
    '1_<1yr': 7,
    '1_1-2yr': 6,
    '1_2-5yr': 5,
    '1_5+yr': 4,
    '0_<1yr': 3,
    '0_1-2yr': 2,
    '0_2-5yr': 1,
    '0_5+yr': 0
}

df_inputs_prepr['delinq_record_risk_score'] = df_inputs_prepr['delinq_record_combo'].map(risk_map)

# List of the Loan Information features
num_features = ['min_mths_since_delinquency', 'delinq_record_risk_score', 'delinq_2yrs', 'collections_12_mths_ex_med',
                'chargeoff_within_12_mths', 'delinq_amnt', 'num_accts_ever_120_pd',
                'num_tl_90g_dpd_24m', 'num_tl_120dpd_2m', 'num_tl_30dpd']

# Calculate and print Variance Inflation Factor (VIF)
print(calculate_vif(df_inputs_prepr, num_features))

                      feature       VIF
0          num_tl_90g_dpd_24m  1.846076
1                 delinq_2yrs  1.813648
2    delinq_record_risk_score  1.583014
3            num_tl_120dpd_2m  1.526392
4                 delinq_amnt  1.420302
5                num_tl_30dpd  1.411802
6       num_accts_ever_120_pd  1.166534
7    chargeoff_within_12_mths  1.056240
8  collections_12_mths_ex_med  1.016597
9  min_mths_since_delinquency  1.012894

# List of the Loan Information features
num_features = ['revol_bal', 'total_bal_il', 'max_bal_bc', 'avg_cur_bal', 'revol_util', 'il_util',
                'all_util', 'bc_util','bc_open_to_buy', 'total_bal_ex_mort', 'total_bc_limit']

# Calculate and print Variance Inflation Factor (VIF)
print(calculate_vif(df_inputs_prepr, num_features))

              feature        VIF
0            all_util  36.721871
1             il_util  28.903446
2             bc_util  19.463804
3          revol_util  19.446893
4      total_bc_limit  14.343601
5      bc_open_to_buy   9.326915
6   total_bal_ex_mort   5.450999
7           revol_bal   3.475286
8        total_bal_il   2.768232
9          max_bal_bc   2.050169
10        avg_cur_bal   1.905737

# Utilization Ratios
df_inputs_prepr['revol_bal_to_bc_limit'] = df_inputs_prepr['revol_bal'] / df_inputs_prepr['total_bc_limit'].replace(0, np.nan)
df_inputs_prepr['revol_bal_to_open_to_buy'] = df_inputs_prepr['revol_bal'] / df_inputs_prepr['bc_open_to_buy'].replace(0, np.nan)

# Balance to Income (if annual_inc is available):
df_inputs_prepr['total_bal_ex_mort_to_inc'] = df_inputs_prepr['total_bal_ex_mort'] / df_inputs_prepr['annual_inc'].replace(0, np.nan)

# List of the Loan Information features
num_features = ['revol_bal', 'total_bal_il', 'max_bal_bc', 'avg_cur_bal', 'bc_open_to_buy', 'revol_bal_to_bc_limit', 
                'revol_bal_to_open_to_buy', 'total_bal_ex_mort_to_inc']

# Calculate and print Variance Inflation Factor (VIF)
print(calculate_vif(df_inputs_prepr, num_features))

                    feature       VIF
0                 revol_bal  2.174330
1  total_bal_ex_mort_to_inc  2.077000
2              total_bal_il  1.819026
3                max_bal_bc  1.768437
4               avg_cur_bal  1.660353
5            bc_open_to_buy  1.414334
6     revol_bal_to_bc_limit  1.245156
7  revol_bal_to_open_to_buy  1.031061

# List of the Loan Information features.
num_features = ['tot_hi_cred_lim', 'total_rev_hi_lim', 'total_il_high_credit_limit', 'tot_cur_bal']

# Calculate and print Variance Inflation Factor (VIF).
print(calculate_vif(df_inputs_prepr, num_features))

                      feature        VIF
0             tot_hi_cred_lim  86.048912
1                 tot_cur_bal  70.228960
2            total_rev_hi_lim   3.281832
3  total_il_high_credit_limit   1.988739

df_inputs_prepr['total_balance_to_credit_ratio'] = df_inputs_prepr['tot_cur_bal'] / df_inputs_prepr['tot_hi_cred_lim'].replace(0, np.nan)

df_inputs_prepr['rev_to_il_limit_ratio'] = df_inputs_prepr['total_rev_hi_lim'] / df_inputs_prepr['total_il_high_credit_limit'].replace(0, np.nan)

# List of the Loan Information features.
num_features = ['total_balance_to_credit_ratio', 'rev_to_il_limit_ratio', 'total_il_high_credit_limit', 'tot_cur_bal']

# Calculate and print Variance Inflation Factor (VIF).
print(calculate_vif(df_inputs_prepr, num_features))

                         feature       VIF
0  total_balance_to_credit_ratio  3.732316
1     total_il_high_credit_limit  3.040626
2                    tot_cur_bal  2.632886
3          rev_to_il_limit_ratio  1.321904

# List of the Loan Information features
num_features = ['open_acc', 'total_acc', 'open_acc_6m', 'open_act_il', 'open_il_12m', 'open_il_24m', 'open_rv_12m', 
                'open_rv_24m', 'num_sats', 'num_il_tl', 'num_rev_accts', 'num_actv_bc_tl', 'num_actv_rev_tl', 'num_bc_sats', 
                'num_bc_tl', 'num_op_rev_tl', 'num_rev_tl_bal_gt_0', 'acc_open_past_24mths', 'total_cu_tl']

# Calculate and print Variance Inflation Factor (VIF)
print(calculate_vif(df_inputs_prepr, num_features))

                 feature         VIF
0    num_rev_tl_bal_gt_0  135.413933
1        num_actv_rev_tl  125.544110
2               num_sats  121.894356
3               open_acc  121.812303
4              total_acc   81.870765
5          num_rev_accts   75.563319
6          num_op_rev_tl   52.440646
7              num_bc_tl   29.505863
8         num_actv_bc_tl   26.112984
9            num_bc_sats   24.070302
10             num_il_tl   15.243944
11  acc_open_past_24mths    6.171402
12           open_rv_12m    6.050756
13           open_rv_24m    5.908277
14           open_il_24m    5.383007
15           open_il_12m    4.179422
16           open_acc_6m    3.607243
17           open_act_il    3.128423
18           total_cu_tl    1.451131

# List of the Loan Information features
num_features = ['total_acc', 'open_act_il', 'open_il_12m',  'num_actv_rev_tl', 'open_rv_12m', 'num_bc_tl',
                'open_acc_6m', 'acc_open_past_24mths', 'total_cu_tl']

# Calculate and print Variance Inflation Factor (VIF)
print(calculate_vif(df_inputs_prepr, num_features))

                feature       VIF
0             total_acc  7.873322
1             num_bc_tl  6.650931
2       num_actv_rev_tl  4.946451
3  acc_open_past_24mths  4.740237
4           open_acc_6m  3.602185
5           open_rv_12m  3.108141
6           open_il_12m  2.296064
7           open_act_il  1.777657
8           total_cu_tl  1.331291

# List of the Loan Information features
num_features =  ['inq_fi', 'inq_last_6mths', 'inq_last_12m', 'mths_since_recent_inq']

# Calculate and print Variance Inflation Factor (VIF)
print(calculate_vif(df_inputs_prepr, num_features))

                 feature       VIF
0           inq_last_12m  2.346682
1                 inq_fi  2.126177
2         inq_last_6mths  1.209716
3  mths_since_recent_inq  1.006674

# List of the features you want to check
num_features = ['out_prncp', 'out_prncp_inv', 'total_pymnt', 'total_pymnt_inv', 'total_rec_prncp', 'total_rec_int', 
                'total_rec_late_fee', 'recoveries', 'collection_recovery_fee', 'last_pymnt_amnt']

# Calculate and print Variance Inflation Factor (VIF)
print(calculate_vif(df_inputs_prepr, num_features))

                   feature           VIF
0              total_pymnt  6.526956e+13
1          total_rec_prncp  4.503600e+13
2            total_rec_int  2.609270e+12
3               recoveries  1.796159e+11
4       total_rec_late_fee  2.310876e+07
5                out_prncp  1.035721e+06
6            out_prncp_inv  1.035720e+06
7          total_pymnt_inv  5.605819e+03
8  collection_recovery_fee  1.083006e+01
9          last_pymnt_amnt  3.216300e+00

df_inputs_prepr['principal_paid_ratio'] = df_inputs_prepr['total_rec_prncp'] / df_inputs_prepr['loan_amnt']

# List of the features you want to check
num_features = ['out_prncp', 'last_pymnt_amnt', 'principal_paid_ratio']

# Calculate and print Variance Inflation Factor (VIF)
print(calculate_vif(df_inputs_prepr, num_features))

                feature       VIF
0  principal_paid_ratio  1.739074
1       last_pymnt_amnt  1.737794
2             out_prncp  1.000991

# List of the features you want to check
num_features =  ['fico_range_low', 'fico_range_high', 'last_fico_range_high', 'last_fico_range_low']

# Calculate and print Variance Inflation Factor (VIF)
print(calculate_vif(df_inputs_prepr, num_features))

                feature           VIF
0       fico_range_high  1.505941e+07
1        fico_range_low  1.505512e+07
2  last_fico_range_high  2.315564e+02
3   last_fico_range_low  7.445950e+01

# List of the features you want to check
num_features =  ['mths_since_earliest_cr_line', 'mths_since_issue_d', 'mo_sin_old_il_acct', 'mo_sin_old_rev_tl_op', 'mo_sin_rcnt_rev_tl_op',
                 'mo_sin_rcnt_tl', 'mths_since_rcnt_il', 'mths_since_recent_bc', 'mths_since_recent_bc_dlq', 'mths_since_recent_revol_delinq']

# Calculate and print Variance Inflation Factor (VIF)
print(calculate_vif(df_inputs_prepr, num_features))

                          feature        VIF
0     mths_since_earliest_cr_line  52.837152
1            mo_sin_old_rev_tl_op  25.293201
2              mths_since_issue_d  18.405433
3        mths_since_recent_bc_dlq  11.219079
4  mths_since_recent_revol_delinq   8.458308
5              mo_sin_old_il_acct   7.728318
6              mths_since_rcnt_il   5.044650
7           mo_sin_rcnt_rev_tl_op   3.561668
8                  mo_sin_rcnt_tl   2.924126
9            mths_since_recent_bc   2.582552

# List of the features you want to check
num_features = ['mths_since_earliest_cr_line', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl', 'mths_since_rcnt_il', 
                'mths_since_recent_bc', 'mths_since_recent_revol_delinq']

# Calculate and print Variance Inflation Factor (VIF)
print(calculate_vif(df_inputs_prepr, num_features))

                          feature       VIF
0     mths_since_earliest_cr_line  4.032080
1           mo_sin_rcnt_rev_tl_op  3.555260
2                  mo_sin_rcnt_tl  2.902862
3              mths_since_rcnt_il  2.572720
4            mths_since_recent_bc  2.571783
5  mths_since_recent_revol_delinq  2.474972

# List of the features you want to check
num_features =  ['percent_bc_gt_75', 'pct_tl_nvr_dlq', 'tax_liens', 'pub_rec', 'pub_rec_bankruptcies', 'tot_coll_amt',
                 'mort_acc', 'months_since_last_pymnt', 'months_since_last_credit_pull', 'policy_code']

# Calculate and print Variance Inflation Factor (VIF)
print(calculate_vif(df_inputs_prepr, num_features))

                         feature         VIF
0                    policy_code  127.367693
1                        pub_rec    7.787948
2           pub_rec_bankruptcies    4.551261
3                      tax_liens    3.970360
4        months_since_last_pymnt    1.463992
5  months_since_last_credit_pull    1.453885
6                 pct_tl_nvr_dlq    1.026930
7                   tot_coll_amt    1.008896
8                       mort_acc    1.005815
9               percent_bc_gt_75    1.004729

# Combining 'pub_rec' and 'tax_liens' 
df_inputs_prepr['total_public_records'] = df_inputs_prepr['pub_rec'] + df_inputs_prepr['tax_liens']

# List of the features you want to check
num_features =  ['percent_bc_gt_75', 'pub_rec_bankruptcies', 'tot_coll_amt', 'mort_acc', 
                 'months_since_last_credit_pull', 'total_public_records']

# Calculate and print Variance Inflation Factor (VIF)
print(calculate_vif(df_inputs_prepr, num_features))

                         feature       VIF
0  months_since_last_credit_pull  2.443156
1               percent_bc_gt_75  2.144920
2                       mort_acc  1.512708
3           pub_rec_bankruptcies  1.476033
4           total_public_records  1.444479
5                   tot_coll_amt  1.010763

# List of features to drop.
feats_num_to_drop = ['funded_amnt', 'funded_amnt_inv', 'installment', 'mths_since_last_delinq', 'mths_since_last_record', 
                     'mths_since_last_major_derog', 'acc_now_delinq', 'has_delinquency_now', 'last_record_bucket', 
                     'delinq_record_combo', 'all_util', 'il_util', 'bc_util', 'revol_util', 'total_bc_limit',  
                     'total_bal_ex_mort', 'tot_hi_cred_lim', 'open_acc', 'open_il_24m', 'open_rv_24m',       
                     'num_sats', 'num_il_tl', 'num_rev_accts', 'num_actv_bc_tl',  'num_bc_sats', 
                     'num_op_rev_tl', 'num_rev_tl_bal_gt_0', 'out_prncp_inv', 'total_pymnt', 'total_pymnt_inv',      
                     'total_rec_prncp', 'total_rec_int', 'total_rec_late_fee', 'recoveries', 'collection_recovery_fee',
                     'out_prncp_inv', 'total_pymnt', 'total_pymnt_inv', 'total_rec_prncp', 'total_rec_int', 
                     'total_rec_late_fee', 'recoveries', 'collection_recovery_fee', 'fico_range_low', 'last_fico_range_low',
                     'mo_sin_old_rev_tl_op', 'mo_sin_old_il_acct', 'mths_since_issue_d', 'mths_since_recent_bc_dlq',
                     'policy_code', 'pub_rec', 'tax_liens', 'months_since_last_pymnt', 'pct_tl_nvr_dlq']
print(len(feats_num_to_drop))

54

# Drop this set of features from the df_inputs_prepr dataframe.
df_inputs_prepr = df_inputs_prepr.drop(columns = feats_num_to_drop)

List_num_features = ['loan_amnt', 'term_int', 'int_rate', 'annual_inc', 'emp_length_int', 'dti', 'min_mths_since_delinquency', 
                     'delinq_record_risk_score', 'delinq_2yrs', 'collections_12_mths_ex_med', 'chargeoff_within_12_mths', 
                     'delinq_amnt', 'num_accts_ever_120_pd', 'num_tl_90g_dpd_24m', 'num_tl_120dpd_2m', 'num_tl_30dpd', 
                     'revol_bal', 'total_bal_il', 'max_bal_bc', 'avg_cur_bal', 'bc_open_to_buy', 'revol_bal_to_bc_limit', 
                     'revol_bal_to_open_to_buy', 'total_bal_ex_mort_to_inc', 'total_balance_to_credit_ratio', 'rev_to_il_limit_ratio', 
                     'total_il_high_credit_limit', 'tot_cur_bal', 'total_acc', 'open_act_il', 'open_il_12m',  'num_actv_rev_tl', 
                     'open_rv_12m', 'num_bc_tl', 'open_acc_6m', 'acc_open_past_24mths', 'total_cu_tl', 'inq_fi', 'inq_last_6mths',
                     'inq_last_12m', 'mths_since_recent_inq', 'out_prncp', 'last_pymnt_amnt', 'principal_paid_ratio',
                     'fico_range_high', 'last_fico_range_high', 'mths_since_earliest_cr_line', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl', 
                     'mths_since_rcnt_il', 'mths_since_recent_bc', 'mths_since_recent_revol_delinq', 'percent_bc_gt_75', 
                     'pub_rec_bankruptcies', 'tot_coll_amt', 'mort_acc', 'months_since_last_credit_pull', 'total_public_records']

print('number of features after preprocessing: ', len(List_num_features))

number of features after preprocessing:  58

# Function to classify the features
def classify_feature(df, threshold_unique=15, threshold_variance=0.01):
    discrete_features = []
    continuous_features = []
    
    for col in df.columns:
        if np.issubdtype(df[col].dtype, np.number):  # only numeric features
            n_unique = df[col].nunique()
            variance = df[col].var()
            
            if np.issubdtype(df[col].dtype, np.integer):
                if n_unique <= threshold_unique or variance < threshold_variance:
                    discrete_features.append(col)
                else:
                    continuous_features.append(col)
            else:  # float
                if n_unique <= 5 or variance < threshold_variance:
                    discrete_features.append(col)
                else:
                    continuous_features.append(col)
    
    return discrete_features, continuous_features

# Assuming df_inputs_prepr is your preprocessed dataset
df_inputs_prepr_class = df_inputs_prepr[List_num_features].copy()
discr_features, conti_features = classify_feature(df_inputs_prepr_class)

print("Discrete features:", discr_features)
print()
print("Continuous features:", conti_features)

Discrete features: ['term_int', 'delinq_record_risk_score', 'num_tl_120dpd_2m', 'num_tl_30dpd']

Continuous features: ['loan_amnt', 'int_rate', 'annual_inc', 'emp_length_int', 'dti', 'min_mths_since_delinquency', 'delinq_2yrs', 'collections_12_mths_ex_med', 'chargeoff_within_12_mths', 'delinq_amnt', 'num_accts_ever_120_pd', 'num_tl_90g_dpd_24m', 'revol_bal', 'total_bal_il', 'max_bal_bc', 'avg_cur_bal', 'bc_open_to_buy', 'revol_bal_to_bc_limit', 'revol_bal_to_open_to_buy', 'total_bal_ex_mort_to_inc', 'total_balance_to_credit_ratio', 'rev_to_il_limit_ratio', 'total_il_high_credit_limit', 'tot_cur_bal', 'total_acc', 'open_act_il', 'open_il_12m', 'num_actv_rev_tl', 'open_rv_12m', 'num_bc_tl', 'open_acc_6m', 'acc_open_past_24mths', 'total_cu_tl', 'inq_fi', 'inq_last_6mths', 'inq_last_12m', 'mths_since_recent_inq', 'out_prncp', 'last_pymnt_amnt', 'principal_paid_ratio', 'fico_range_high', 'last_fico_range_high', 'mths_since_earliest_cr_line', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl', 'mths_since_rcnt_il', 'mths_since_recent_bc', 'mths_since_recent_revol_delinq', 'percent_bc_gt_75', 'pub_rec_bankruptcies', 'tot_coll_amt', 'mort_acc', 'months_since_last_credit_pull', 'total_public_records']

# WoE function for ordered discrete and continuous variables
def woe_ordered_continuous(df, discrete_variabe_name, good_bad_variable_df):
    df = pd.concat([df[discrete_variabe_name], good_bad_variable_df], axis = 1)
    df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
                    df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)
    df = df.iloc[:, [0, 1, 3]]
    df.columns = [df.columns.values[0], 'n_obs', 'prop_good']
    df['prop_n_obs'] = df['n_obs'] / df['n_obs'].sum()
    df['n_good'] = df['prop_good'] * df['n_obs']
    df['n_bad'] = (1 - df['prop_good']) * df['n_obs']
    df['prop_n_good'] = df['n_good'] / df['n_good'].sum()
    df['prop_n_bad'] = df['n_bad'] / df['n_bad'].sum()
    df['WoE'] = np.log1p(df['prop_n_good'] / df['prop_n_bad'])
    #df = df.sort_values(['WoE'])
    #df = df.reset_index(drop = True)
    df['diff_prop_good'] = df['prop_good'].diff().abs()
    df['diff_WoE'] = df['WoE'].diff().abs()
    df['IV'] = (df['prop_n_good'] - df['prop_n_bad']) * df['WoE']
    df['IV'] = df['IV'].sum()
    return df
# Here we define a function similar to the one above, ...
# ... with one slight difference: we order the results by the values of a different column.
# The function takes 3 arguments: a dataframe, a string, and a dataframe. The function returns a dataframe as a result.

df_temp = woe_ordered_continuous(df_inputs_prepr, 'term_int', df_targets_prepr)
# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp)
# We plot the weight of evidence values.

# Leave as is.
# '60' will be the reference category.
df_inputs_prepr['term:36'] = np.where((df_inputs_prepr['term_int'] == 36), 1, 0)
df_inputs_prepr['term:60'] = np.where((df_inputs_prepr['term_int'] == 60), 1, 0)

# 'num_tl_120dpd_2m'
df_inputs_prepr['num_tl_120dpd_2m'].unique()
# Has only 6 levels: from 0 to 6. Hence, we turn it into a factor with 6 levels.

array([0., 1., 2.])

df_temp = woe_ordered_continuous(df_inputs_prepr, 'num_tl_120dpd_2m', df_targets_prepr)
# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp)
# We plot the weight of evidence values.

# We create the following categories: '0', '1', '2 - 6'
# '2-6' will be the reference category
df_inputs_prepr['num_tl_120dpd_2m:0'] = np.where(df_inputs_prepr['num_tl_120dpd_2m'].isin([0]), 1, 0)
df_inputs_prepr['num_tl_120dpd_2m:1'] = np.where(df_inputs_prepr['num_tl_120dpd_2m'].isin([1]), 1, 0)
df_inputs_prepr['num_tl_120dpd_2m:2-6'] = np.where(df_inputs_prepr['num_tl_120dpd_2m'].isin(range(2, 7)), 1, 0)

# 'num_tl_30dpd'
df_inputs_prepr['num_tl_30dpd'].unique()
# Has only 6 levels: from 0 to 6. Hence, we turn it into a factor with 6 levels.

array([0., 2., 1., 3.])

df_temp = woe_ordered_continuous(df_inputs_prepr, 'num_tl_30dpd', df_targets_prepr)
# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp)
# We plot the weight of evidence values.

# We create the following categories: '0 - 2', '3', '4'
# '4' will be the reference category
df_inputs_prepr['num_tl_30dpd:0'] = np.where(df_inputs_prepr['num_tl_30dpd'].isin([0]), 1, 0)
df_inputs_prepr['num_tl_30dpd:1'] = np.where(df_inputs_prepr['num_tl_30dpd'].isin([1]), 1, 0)
df_inputs_prepr['num_tl_30dpd:2-4'] = np.where(df_inputs_prepr['num_tl_30dpd'].isin(range(2, 5)), 1, 0)

# 'num_tl_30dpd'
df_inputs_prepr['delinq_record_risk_score'].unique()
# Has only 6 levels: from 0 to 6. Hence, we turn it into a factor with 6 levels.

array([0, 2, 1, 4, 3, 5, 7, 6], dtype=int64)

df_temp = woe_ordered_continuous(df_inputs_prepr, 'delinq_record_risk_score', df_targets_prepr)
# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp)
# We plot the weight of evidence values.

# We create the following categories: '0', '1 - 2', '3 - 4', '5 - 7'
# '5-7' will be the reference category
df_inputs_prepr['delinq_record_risk_score:0'] = np.where(df_inputs_prepr['delinq_record_risk_score'].isin([0]), 1, 0)
df_inputs_prepr['delinq_record_risk_score:1-2'] = np.where(df_inputs_prepr['delinq_record_risk_score'].isin(range(1, 3)), 1, 0)
df_inputs_prepr['delinq_record_risk_score:3-4'] = np.where(df_inputs_prepr['delinq_record_risk_score'].isin(range(3, 5)), 1, 0)
df_inputs_prepr['delinq_record_risk_score:5-7'] = np.where(df_inputs_prepr['delinq_record_risk_score'].isin(range(5, 8)), 1, 0)

# loan_amnt
df_inputs_prepr['loan_amnt_factor'] = pd.cut(df_inputs_prepr['loan_amnt'], 50)
# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr, 'loan_amnt_factor', df_targets_prepr)
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories:
# < 2500, 2500 - 6500, 6500 - 9500, 9500 - 10800, 10800 - 17500, 17500 - 28500, >= 28500.
df_inputs_prepr['loan_amnt:<2500'] = np.where((df_inputs_prepr['loan_amnt'] <= 2500.), 1, 0)
df_inputs_prepr['loan_amnt:2500-6500'] = np.where((df_inputs_prepr['loan_amnt'] > 2500.) & (df_inputs_prepr['loan_amnt'] <= 6500.), 1, 0)
df_inputs_prepr['loan_amnt:6500-9500'] = np.where((df_inputs_prepr['loan_amnt'] > 6500.) & (df_inputs_prepr['loan_amnt'] <= 9500.), 1, 0)
df_inputs_prepr['loan_amnt:9500-11000'] = np.where((df_inputs_prepr['loan_amnt'] > 9500.) & (df_inputs_prepr['loan_amnt'] <= 10800.), 1, 0)
df_inputs_prepr['loan_amnt:11000-17500'] = np.where((df_inputs_prepr['loan_amnt'] > 10800.) & (df_inputs_prepr['loan_amnt'] <= 17500.), 1, 0)
df_inputs_prepr['loan_amnt:17500-28500'] = np.where((df_inputs_prepr['loan_amnt'] > 17500.) & (df_inputs_prepr['loan_amnt'] <= 28500.), 1, 0)
df_inputs_prepr['loan_amnt:>=28500'] = np.where((df_inputs_prepr['loan_amnt'] > 28500.), 1, 0)

# Drop 'loan_amnt_factor' feature
df_inputs_prepr = df_inputs_prepr.drop(columns = ['loan_amnt_factor'])

# unique values of 'int_rate'
df_inputs_prepr['int_rate'].unique()

array([13.99, 18.06, 12.29,  7.89, 21.6 , 16.29, 14.33, 14.64, 14.49,
       24.99, 18.49, 10.15,  5.32, 13.49, 19.03,  8.18,  9.67, 12.49,
       15.61, 21.97, 11.53, 16.99,  9.99,  6.92, 16.02,  6.03, 10.91,
       12.12, 13.35,  6.97,  7.9 ,  7.49,  7.62,  8.19, 11.49, 11.47,
       10.64,  8.9 , 19.99, 14.99, 10.99,  8.49, 13.53, 19.24,  9.16,
        8.67, 11.99, 17.86, 30.75,  9.44, 12.69, 13.11, 12.74, 16.01,
       14.46, 11.55, 14.16, 20.2 , 15.1 , 18.99, 15.59, 21.49, 13.59,
       11.67,  9.17, 26.49,  8.99,  7.97,  9.91, 13.67,  7.39, 19.52,
        8.24, 28.67, 19.97,  9.93,  8.39, 14.08,  6.49, 12.99, 13.18,
        6.62, 16.46, 17.57, 14.31, 15.49,  7.91, 25.69, 20.99,  9.76,
       18.25,  7.35, 13.65, 23.28,  6.72, 14.03, 17.09, 12.62, 12.79,
       17.27,  7.69,  9.75, 17.99, 10.38,  7.26, 22.47, 10.75,  7.84,
       16.55,  9.92, 10.49, 13.66, 14.65, 18.55, 13.98,  7.99,  9.8 ,
       15.31, 24.74, 30.79, 24.7 , 11.22, 21.48, 14.47, 11.86, 15.41,
       11.97, 15.88, 13.33, 16.24, 17.1 , 12.18, 12.35,  6.24,  8.38,
       20.31, 14.09, 23.99, 20.89, 18.24,  7.34,  7.02, 19.05, 21.99,
       19.19, 10.42, 11.14,  6.91, 15.77, 17.56,  6.08,  7.46, 16.2 ,
        6.68, 30.99, 21.  , 29.69,  9.58, 22.35, 11.39,  7.51, 22.15,
       19.48, 17.58, 20.39, 13.06, 15.05, 15.8 , 24.84,  6.89, 20.5 ,
        6.39, 11.71,  6.  , 12.05, 18.75, 24.49, 14.98, 11.98, 20.  ,
       13.44, 22.95, 30.84, 18.84, 10.  , 25.89, 19.53, 22.74, 24.5 ,
       12.42,  9.49, 23.7 , 11.06, 24.85, 12.73, 10.56, 15.95, 22.39,
       26.77, 14.26, 26.57, 22.99,  8.59, 10.16, 18.54, 11.44, 10.78,
       25.81, 17.76, 15.99,  5.93, 19.22,  6.71,  5.99, 19.42,  7.07,
       30.17, 12.59, 21.45, 19.72, 17.77, 21.67, 22.9 ,  5.31, 17.47,
        7.12, 18.2 , 16.14, 22.7 , 15.22, 13.85, 20.25, 10.41, 10.08,
       27.27, 12.88, 21.36, 21.85,  7.66, 16.7 ,  7.14, 28.69, 25.29,
       20.75, 20.49, 29.49, 15.02, 14.3 ,  7.37,  6.99, 12.39, 16.59,
       19.89,  6.07, 11.48, 22.4 , 14.84, 16.49, 13.68, 30.94, 15.27,
        7.24, 12.98,  8.94, 14.07, 19.47, 12.85,  6.54, 14.48, 23.43,
        9.71,  7.21, 10.74, 13.56, 13.72, 19.16, 19.2 , 12.61, 17.14,
       25.99,  7.96,  8.6 ,  7.74, 13.05, 26.24, 11.11,  9.43, 17.49,
       26.06, 21.18, 14.85, 20.8 , 21.7 , 28.99, 26.3 , 17.97, 19.29,
       23.1 , 22.45, 13.57, 18.85, 28.72, 27.31, 25.82,  7.88, 27.34,
       17.93, 21.98, 18.45, 11.26, 23.76, 23.26, 18.92, 15.04, 13.8 ,
       14.52, 14.17, 30.89, 16.45, 28.49, 16.77, 23.88, 23.63, 15.23,
       10.47, 24.37, 25.78, 25.09, 28.88, 22.2 , 12.13, 12.84,  9.63,
       25.57, 25.49, 23.5 ,  8.08, 14.27,  7.59, 22.78, 24.08, 13.61,
       21.28, 18.94, 21.15, 19.69, 25.83, 25.88, 29.99, 23.13, 16.69,
       30.65, 27.79,  6.19, 16.91,  8.32, 25.28, 15.96, 13.58, 12.87,
       19.92, 10.37, 11.36, 13.16, 18.64,  8.46, 23.4 , 15.21,  7.29,
       19.13, 15.81, 26.99, 24.89, 16.33, 30.49, 12.68, 16.78,  7.4 ,
        9.45,  8.88,  6.67, 22.91, 10.95, 16.  , 10.59, 25.8 , 12.53,
       13.23, 19.79, 23.32, 24.11,  9.25, 14.22, 14.91,  6.17, 25.34,
       13.24, 11.05,  7.56,  5.79, 27.49,  9.88, 13.92, 24.24, 24.83,
        5.42, 10.9 , 25.44, 11.89, 13.79, 15.68, 11.8 ,  9.33, 10.65,
       16.32, 14.35, 15.62, 20.9 , 14.72, 11.83, 23.87, 18.79, 26.14,
       28.18, 12.92, 15.2 , 19.91,  6.83, 20.62, 23.91, 26.31, 17.43,
       16.4 , 15.28, 10.33, 17.46, 10.62, 15.7 , 14.11, 10.07, 30.74,
       10.36, 16.95, 12.23,  6.11, 10.25,  9.07, 28.14, 11.12, 11.91,
       23.59,  9.32, 15.33,  9.2 , 11.34, 20.16, 12.09, 17.8 , 14.42,
       25.11, 24.33, 10.28, 27.88, 17.39,  9.62, 13.48, 15.57, 23.83,
       27.99, 18.39, 14.79, 13.22, 14.59, 14.83, 14.54, 13.47,  8.81,
       17.74, 28.34,  8.  , 16.89,  6.46,  9.7 , 14.96, 16.35, 11.28,
       14.74,  9.83, 18.3 , 13.43, 23.33,  6.76, 11.66, 10.72, 15.65,
        7.68, 12.41, 11.58, 17.88, 20.3 , 16.82, 15.58, 10.39, 17.51,
        7.43, 10.71, 19.82, 10.83, 11.63, 13.04, 19.74, 17.19, 22.11,
       11.31, 18.67, 11.46, 12.21, 16.07, 18.78, 22.06,  8.63, 11.54,
       19.41, 11.03, 19.36, 18.62,  9.64, 21.59, 24.2 , 29.67, 15.37,
       29.96, 20.48, 19.04, 23.52, 10.14, 11.59, 22.48, 13.3 ,  7.75,
       17.06,  8.7 , 25.65, 18.72, 20.11, 10.51, 14.61, 15.13, 18.91,
       11.09, 17.04, 20.53, 19.66, 12.72, 13.17, 21.74, 16.63, 10.46,
       24.52, 18.07,  7.05,  8.07, 12.54, 12.8 , 18.43, 14.75, 12.22,
       18.17, 21.14, 20.85, 14.18, 10.96, 20.03, 16.08, 11.78, 13.75,
       15.45,  9.38, 11.41, 12.36,  9.01, 20.77, 21.27, 14.93, 23.22,
       13.55, 24.76, 12.67, 17.26, 17.34, 16.11, 12.04, 12.17,  7.42,
       12.86, 14.82, 21.82, 13.62, 10.2 , 17.15,  9.51])

# loan_amnt
df_inputs_prepr['int_rate_factor'] = pd.cut(df_inputs_prepr['int_rate'], 50)
# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr, 'int_rate_factor', df_targets_prepr)
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories:
# < 8, 8 - 12.5, 12.5 - 16.5, 16.5 - 20, 20 - 23.5, >= 23.5
df_inputs_prepr['int_rate:<=8'] = np.where((df_inputs_prepr['int_rate'] <= 8.0), 1, 0)
df_inputs_prepr['int_rate:8-12.5'] = np.where((df_inputs_prepr['int_rate'] > 8.0) & (df_inputs_prepr['int_rate'] <= 12.5), 1, 0)
df_inputs_prepr['int_rate:12.5-16.5'] = np.where((df_inputs_prepr['int_rate'] > 12.5) & (df_inputs_prepr['int_rate'] <= 16.5), 1, 0)
df_inputs_prepr['int_rate:16.5-20'] = np.where((df_inputs_prepr['int_rate'] > 16.5) & (df_inputs_prepr['int_rate'] <= 20.0), 1, 0)
df_inputs_prepr['int_rate:20-23.5'] = np.where((df_inputs_prepr['int_rate'] > 20.0) & (df_inputs_prepr['int_rate'] <= 23.5), 1, 0)
df_inputs_prepr['int_rate:>23.5'] = np.where((df_inputs_prepr['int_rate'] > 23.5), 1, 0)

# Drop 'loan_amnt_factor' feature
df_inputs_prepr = df_inputs_prepr.drop(columns = ['int_rate_factor'])

# annual_inc
df_inputs_prepr['annual_inc_factor'] = pd.cut(df_inputs_prepr['annual_inc'], 75)
# Here we do fine-classing: using the 'cut' method, we split the variable into 100 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr, 'annual_inc_factor', df_targets_prepr)
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

# Initial examination shows that there are too few individuals with large income and too many with small income.
# Hence, we are going to have one category for more than 140K, and we are going to apply our approach to determine
# the categories of everyone with 140k or less.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['annual_inc'] <= 140000., : ]
#loan_data_temp = loan_data_temp.reset_index(drop = True)
#df_inputs_prepr_temp
df_inputs_prepr_temp["annual_inc_factor"] = pd.cut(df_inputs_prepr_temp['annual_inc'], 50)
# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'annual_inc_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3753315463.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp["annual_inc_factor"] = pd.cut(df_inputs_prepr_temp['annual_inc'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# WoE is monotonically decreasing with income, so we split income in 10 equal categories
df_inputs_prepr['log_annual_inc:<20K'] = np.where((df_inputs_prepr['annual_inc'] <= 20000), 1, 0)
df_inputs_prepr['annual_inc:20K-30K'] = np.where((df_inputs_prepr['annual_inc'] > 20000) & (df_inputs_prepr['annual_inc'] <= 30000), 1, 0)
df_inputs_prepr['annual_inc:30K-40K'] = np.where((df_inputs_prepr['annual_inc'] > 30000) & (df_inputs_prepr['annual_inc'] <= 40000), 1, 0)
df_inputs_prepr['annual_inc:40K-50K'] = np.where((df_inputs_prepr['annual_inc'] > 40000) & (df_inputs_prepr['annual_inc'] <= 50000), 1, 0)
df_inputs_prepr['annual_inc:50K-60K'] = np.where((df_inputs_prepr['annual_inc'] > 50000) & (df_inputs_prepr['annual_inc'] <= 60000), 1, 0)
df_inputs_prepr['annual_inc:60K-70K'] = np.where((df_inputs_prepr['annual_inc'] > 60000) & (df_inputs_prepr['annual_inc'] <= 70000), 1, 0)
df_inputs_prepr['annual_inc:70K-80K'] = np.where((df_inputs_prepr['annual_inc'] > 70000) & (df_inputs_prepr['annual_inc'] <= 80000), 1, 0)
df_inputs_prepr['annual_inc:80K-90K'] = np.where((df_inputs_prepr['annual_inc'] > 80000) & (df_inputs_prepr['annual_inc'] <= 90000), 1, 0)
df_inputs_prepr['annual_inc:90K-100K'] = np.where((df_inputs_prepr['annual_inc'] > 90000) & (df_inputs_prepr['annual_inc'] <= 100000), 1, 0)
df_inputs_prepr['annual_inc:100K-120K'] = np.where((df_inputs_prepr['annual_inc'] > 100000) & (df_inputs_prepr['annual_inc'] <= 120000), 1, 0)
df_inputs_prepr['annual_inc:120K-140K'] = np.where((df_inputs_prepr['annual_inc'] > 120000) & (df_inputs_prepr['annual_inc'] <= 140000), 1, 0)
df_inputs_prepr['annual_inc:>140K'] = np.where((df_inputs_prepr['annual_inc'] > 140000), 1, 0)

df_inputs_prepr = df_inputs_prepr.drop(columns = ['annual_inc_factor'])
# Drop the provisory feature

df_inputs_prepr['emp_length_int'].unique()

array([ 7.,  2.,  0., 10.,  8.,  1.,  3.,  5.,  9.,  4.,  6.])

# mths_since_issue_d
df_temp = woe_ordered_continuous(df_inputs_prepr, 'emp_length_int', df_targets_prepr)
# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp)
# We plot the weight of evidence values, rotating the labels 90 degrees.

# We create the following categories:
# 0 , 1, 2 - 4, 5 - 7, 8 - 9, 10
df_inputs_prepr['emp_length_int:0'] = np.where(df_inputs_prepr['emp_length_int'].isin([0]), 1, 0)
df_inputs_prepr['emp_length_int:1'] = np.where(df_inputs_prepr['emp_length_int'].isin([1]), 1, 0)
df_inputs_prepr['emp_length_int:2-4'] = np.where(df_inputs_prepr['emp_length_int'].isin(range(2, 5)), 1, 0)
df_inputs_prepr['emp_length_int:5-7'] = np.where(df_inputs_prepr['emp_length_int'].isin(range(5, 8)), 1, 0)
df_inputs_prepr['emp_length_int:8-9'] = np.where(df_inputs_prepr['emp_length_int'].isin(range(8, 10)), 1, 0)
df_inputs_prepr['emp_length_int:10'] = np.where(df_inputs_prepr['emp_length_int'].isin([10]), 1, 0)

# int_rate
df_inputs_prepr['dti_factor'] = pd.cut(df_inputs_prepr['dti'], 50)
# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.

df_temp = woe_ordered_continuous(df_inputs_prepr, 'dti_factor', df_targets_prepr)
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# one category for everyone with 'dti' higher than 40 is considered. 
# the categories of everyone with 'dti' less or equal 40.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['dti'] <= 40., : ]
#loan_data_temp = loan_data_temp.reset_index(drop = True)
#df_inputs_prepr_temp
df_inputs_prepr_temp["dti_factor"] = pd.cut(df_inputs_prepr_temp['dti'], 2)
# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'dti_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3006790741.py:6: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp["dti_factor"] = pd.cut(df_inputs_prepr_temp['dti'], 2)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories:  '<= 10.', '10. - 20.', '20. - 30.', '30. - 40.', (> 40.)
df_inputs_prepr['dti:<=10'] = np.where((df_inputs_prepr['dti'] <= 10.), 1, 0)
df_inputs_prepr['dti:10-20'] = np.where((df_inputs_prepr['dti'] > 10.) & (df_inputs_prepr['dti'] <= 20.), 1, 0)
df_inputs_prepr['dti:20-30'] = np.where((df_inputs_prepr['dti'] > 20.) & (df_inputs_prepr['dti'] <= 30.), 1, 0)
df_inputs_prepr['dti:30-40'] = np.where((df_inputs_prepr['dti'] > 30.) & (df_inputs_prepr['dti'] <= 40.), 1, 0)
df_inputs_prepr['dti:>40'] = np.where((df_inputs_prepr['dti'] > 40.), 1, 0)

df_inputs_prepr = df_inputs_prepr.drop(columns = ['dti_factor'])

# one category will be created for 'min_mths_since_delinquency' = 999 corresponds to missing values. 
#***********************************************************************************************
# the categories of everyone with 'min_mths_since_delinquency' less 300 (max value = 226).
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['min_mths_since_delinquency'] <= 500, : ]
#loan_data_temp = loan_data_temp.reset_index(drop = True)
#df_inputs_prepr_temp
df_inputs_prepr_temp['min_mths_since_delinquency_factor'] = pd.cut(df_inputs_prepr_temp['min_mths_since_delinquency'], 50)
# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'min_mths_since_delinquency_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1149940471.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['min_mths_since_delinquency_factor'] = pd.cut(df_inputs_prepr_temp['min_mths_since_delinquency'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

## We create the following categories:
# 'Missing', # <=20, # 20 - 40, # 40 - 80, # >80
df_inputs_prepr['min_mths_since_delinquency:Missing'] = np.where(df_inputs_prepr['min_mths_since_delinquency'].isin([999]), 1, 0)
df_inputs_prepr['min_mths_since_delinquency:<=20'] = np.where(df_inputs_prepr['min_mths_since_delinquency'].isin(range(21)), 1, 0)
df_inputs_prepr['min_mths_since_delinquency:20-40'] = np.where(df_inputs_prepr['min_mths_since_delinquency'].isin(range(21, 41)), 1, 0)
df_inputs_prepr['min_mths_since_delinquency:40-80'] = np.where(df_inputs_prepr['min_mths_since_delinquency'].isin(range(41, 81)), 1, 0)
df_inputs_prepr['min_mths_since_delinquency:>80'] = np.where(df_inputs_prepr['min_mths_since_delinquency'].isin(range(81, int(df_inputs_prepr_temp['mths_since_earliest_cr_line'].max()))), 1, 0)

# mths_since_earliest_cr_line
df_inputs_prepr['mths_since_earliest_cr_line_factor'] = pd.cut(df_inputs_prepr['mths_since_earliest_cr_line'], 45)
# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr, 'mths_since_earliest_cr_line_factor', df_targets_prepr)
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories:
# <= 120, # 121 - 200, # 201 - 260, # 261 - 320, # 320 - 400, # 401 - 600, # => 601
df_inputs_prepr['mths_since_earliest_cr_line:<=120'] = np.where(df_inputs_prepr['mths_since_earliest_cr_line'].isin(range(121)), 1, 0)
df_inputs_prepr['mths_since_earliest_cr_line:121-200'] = np.where(df_inputs_prepr['mths_since_earliest_cr_line'].isin(range(121, 201)), 1, 0)
df_inputs_prepr['mths_since_earliest_cr_line:201-260'] = np.where(df_inputs_prepr['mths_since_earliest_cr_line'].isin(range(201,261)), 1, 0)
df_inputs_prepr['mths_since_earliest_cr_line:261-320'] = np.where(df_inputs_prepr['mths_since_earliest_cr_line'].isin(range(261, 321)), 1, 0)
df_inputs_prepr['mths_since_earliest_cr_line:321-400'] = np.where(df_inputs_prepr['mths_since_earliest_cr_line'].isin(range(321, 401)), 1, 0)
df_inputs_prepr['mths_since_earliest_cr_line:401-600'] = np.where(df_inputs_prepr['mths_since_earliest_cr_line'].isin(range(401, 601)), 1, 0)
df_inputs_prepr['mths_since_earliest_cr_line:>=601'] = np.where(df_inputs_prepr['mths_since_earliest_cr_line'].isin(range(601, int(df_inputs_prepr['mths_since_earliest_cr_line'].max()))), 1, 0)

df_inputs_prepr = df_inputs_prepr.drop(columns = ['mths_since_earliest_cr_line_factor'])
# Drop the provisory feature

df_inputs_prepr['delinq_2yrs'].unique()

array([ 0.,  1.,  2.,  3.,  4.,  5., 14.,  7.,  6.,  9., 10.,  8., 13.,
       11., 12., 18., 16., 15., 20., 17., 19., 36., 26., 27., 22., 24.])

# delinq_2yrs
df_temp = woe_ordered_continuous(df_inputs_prepr, 'delinq_2yrs', df_targets_prepr)
# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp)
# We plot the weight of evidence values.

# Categories: 0, 1, 2-9, >=10
df_inputs_prepr['delinq_2yrs:0'] = np.where((df_inputs_prepr['delinq_2yrs'] == 0), 1, 0)
df_inputs_prepr['delinq_2yrs:1'] = np.where((df_inputs_prepr['delinq_2yrs'] == 1), 1, 0)
df_inputs_prepr['delinq_2yrs:2-9'] = np.where((df_inputs_prepr['delinq_2yrs'] >= 2) & (df_inputs_prepr['delinq_2yrs'] <= 9), 1, 0)
df_inputs_prepr['delinq_2yrs:>=10'] = np.where((df_inputs_prepr['delinq_2yrs'] >= 10), 1, 0)

df_inputs_prepr['inq_last_6mths'].unique()

array([1., 2., 0., 3., 6., 4., 5., 7., 8.])

# inq_last_6mths
df_temp = woe_ordered_continuous(df_inputs_prepr, 'inq_last_6mths', df_targets_prepr)
# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp)
# We plot the weight of evidence values.

# Categories: 0, 1 - 2, 3 - 5, >= 6
df_inputs_prepr['inq_last_6mths:0'] = np.where((df_inputs_prepr['inq_last_6mths'] == 0), 1, 0)
df_inputs_prepr['inq_last_6mths:1-2'] = np.where((df_inputs_prepr['inq_last_6mths'] >= 1) & (df_inputs_prepr['inq_last_6mths'] <= 2), 1, 0)
df_inputs_prepr['inq_last_6mths:3-5'] = np.where((df_inputs_prepr['inq_last_6mths'] >= 3) & (df_inputs_prepr['inq_last_6mths'] <= 5), 1, 0)
df_inputs_prepr['inq_last_6mths:>=6'] = np.where((df_inputs_prepr['inq_last_6mths'] >= 6), 1, 0)

df_inputs_prepr['collections_12_mths_ex_med'].unique()

array([ 0.,  1.,  2.,  3.,  7.,  4.,  6.,  5., 14.,  9.])

# open_acc
df_temp = woe_ordered_continuous(df_inputs_prepr, 'collections_12_mths_ex_med', df_targets_prepr)
# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '0', '1', '>=2'
df_inputs_prepr['collections_12_mths_ex_med:0'] = np.where((df_inputs_prepr['collections_12_mths_ex_med'] == 0), 1, 0)
df_inputs_prepr['collections_12_mths_ex_med:1'] = np.where((df_inputs_prepr['collections_12_mths_ex_med'] == 1), 1, 0)
df_inputs_prepr['collections_12_mths_ex_med:>=2'] = np.where((df_inputs_prepr['collections_12_mths_ex_med'] >= 2), 1, 0)

df_inputs_prepr['chargeoff_within_12_mths'].unique()

array([0., 1., 3., 2., 4., 5., 6., 8., 7.])

# pub_rec
df_temp = woe_ordered_continuous(df_inputs_prepr, 'chargeoff_within_12_mths', df_targets_prepr)
# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories '0-2', '3-4', '>=5'
df_inputs_prepr['chargeoff_within_12_mths:0'] = np.where((df_inputs_prepr['chargeoff_within_12_mths'] == 0), 1, 0)
df_inputs_prepr['chargeoff_within_12_mths:1'] = np.where((df_inputs_prepr['chargeoff_within_12_mths'] == 1), 1, 0)
df_inputs_prepr['chargeoff_within_12_mths:>=2'] = np.where((df_inputs_prepr['chargeoff_within_12_mths'] >= 2), 1, 0)

# total_acc
df_inputs_prepr['total_acc_factor'] = pd.cut(df_inputs_prepr['total_acc'], 58)
# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr, 'total_acc_factor', df_targets_prepr)
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '<=20', '20-56', '>=57'
df_inputs_prepr['total_acc:<=20'] = np.where((df_inputs_prepr['total_acc'] <= 20), 1, 0)
df_inputs_prepr['total_acc:21-56'] = np.where((df_inputs_prepr['total_acc'] >= 21) & (df_inputs_prepr['total_acc'] <= 56), 1, 0)
df_inputs_prepr['total_acc:>=57'] = np.where((df_inputs_prepr['total_acc'] >= 57), 1, 0)

df_inputs_prepr = df_inputs_prepr.drop(columns = ['total_acc_factor'])
# Drop the provisory feature

# unique values
df_inputs_prepr['delinq_amnt'].nunique()

# number of observations with 0 value
df_inputs_prepr['delinq_amnt'].value_counts()[0]

# 'delinq_amnt'
df_inputs_prepr['delinq_amnt_factor'] = pd.cut(df_inputs_prepr['delinq_amnt'], 50)
# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.

# acc_now_delinq
df_temp = woe_ordered_continuous(df_inputs_prepr, 'delinq_amnt_factor', df_targets_prepr)
# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '0', '>=1'
df_inputs_prepr['delinq_amnt:0'] = np.where((df_inputs_prepr['delinq_amnt'] == 0), 1, 0)
df_inputs_prepr['delinq_amnt:>=1'] = np.where((df_inputs_prepr['delinq_amnt'] >= 1), 1, 0)

df_inputs_prepr = df_inputs_prepr.drop(columns = ['delinq_amnt_factor'])
# Drop the provisory feature

# unique values
df_inputs_prepr['num_accts_ever_120_pd'].unique()

array([ 0.,  2.,  1.,  3.,  4., 16.,  7.,  5., 18.,  9., 11.,  6., 13.,
       12., 23., 10.,  8., 15., 14., 26., 20., 34., 19., 17., 27., 24.,
       22., 28., 25., 29., 21., 30.])

# 'num_accts_ever_120_pd'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'num_accts_ever_120_pd', df_targets_prepr)

# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '0', '1-11', '>=12'
df_inputs_prepr['num_accts_ever_120_pd:0'] = np.where((df_inputs_prepr['num_accts_ever_120_pd'] == 0), 1, 0)
df_inputs_prepr['num_accts_ever_120_pd:1-11'] = np.where((df_inputs_prepr['num_accts_ever_120_pd'] >= 1) & (df_inputs_prepr['num_accts_ever_120_pd'] <= 11), 1, 0)
df_inputs_prepr['num_accts_ever_120_pd:>=12'] = np.where((df_inputs_prepr['num_accts_ever_120_pd'] >= 12), 1, 0)

# unique values
df_inputs_prepr['num_tl_90g_dpd_24m'].unique()

array([ 0.,  1.,  2.,  4., 13.,  3.,  9.,  6., 14.,  5.,  7.,  8., 11.,
       10., 12., 18., 15., 20., 16., 36., 26., 22., 24., 17.])

# 'num_tl_90g_dpd_24m'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'num_tl_90g_dpd_24m', df_targets_prepr)

# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '0', '1-4', '>=5'
df_inputs_prepr['num_tl_90g_dpd_24m:0'] = np.where((df_inputs_prepr['num_tl_90g_dpd_24m'] == 0), 1, 0)
df_inputs_prepr['num_tl_90g_dpd_24m:1-4'] = np.where((df_inputs_prepr['num_tl_90g_dpd_24m'] >= 1) & (df_inputs_prepr['num_tl_90g_dpd_24m'] <= 4), 1, 0)
df_inputs_prepr['num_tl_90g_dpd_24m:>=5'] = np.where((df_inputs_prepr['num_tl_90g_dpd_24m'] >= 5), 1, 0)

# unique values
df_inputs_prepr['revol_bal'].unique()

array([ 11405.,  30808.,  16940., ...,  87095., 155670.,  34577.])

# 'revol_bal'
df_inputs_prepr['revol_bal_factor'] = pd.cut(df_inputs_prepr['revol_bal'], 50)
# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.

# 'revol_bal'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'revol_bal_factor', df_targets_prepr)
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# one category will be created for 'revol_bal' > 100000. 
#***********************************************************************************************
# the categories of everyone with 'revol_bal' less 100000.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['revol_bal'] <= 100000, : ]

#df_inputs_prepr_temp
df_inputs_prepr_temp['revol_bal_factor'] = pd.cut(df_inputs_prepr_temp['revol_bal'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'revol_bal_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3812317764.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['revol_bal_factor'] = pd.cut(df_inputs_prepr_temp['revol_bal'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '<= 8000', '8000-22000', '22000-35000', '35000-60000', '60000-100000', > 100000
df_inputs_prepr['revol_bal:<=8k'] = np.where((df_inputs_prepr['revol_bal'] <= 8000.), 1, 0)
df_inputs_prepr['revol_bal:8-22k'] = np.where((df_inputs_prepr['revol_bal'] > 8000.) & (df_inputs_prepr['revol_bal'] <= 22000.), 1, 0)
df_inputs_prepr['revol_bal:22-35k'] = np.where((df_inputs_prepr['revol_bal'] > 22000.) & (df_inputs_prepr['revol_bal'] <= 35000.), 1, 0)
df_inputs_prepr['revol_bal:35-60k'] = np.where((df_inputs_prepr['revol_bal'] > 35000.) & (df_inputs_prepr['revol_bal'] <= 60000.), 1, 0)
df_inputs_prepr['revol_bal:60-100k'] = np.where((df_inputs_prepr['revol_bal'] > 60000.) & (df_inputs_prepr['revol_bal'] <= 100000.), 1, 0)
df_inputs_prepr['revol_bal:>100k'] = np.where((df_inputs_prepr['revol_bal'] > 100000.), 1, 0)

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3187895681.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['revol_bal:22-35k'] = np.where((df_inputs_prepr['revol_bal'] > 22000.) & (df_inputs_prepr['revol_bal'] <= 35000.), 1, 0)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3187895681.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['revol_bal:35-60k'] = np.where((df_inputs_prepr['revol_bal'] > 35000.) & (df_inputs_prepr['revol_bal'] <= 60000.), 1, 0)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3187895681.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['revol_bal:60-100k'] = np.where((df_inputs_prepr['revol_bal'] > 60000.) & (df_inputs_prepr['revol_bal'] <= 100000.), 1, 0)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3187895681.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['revol_bal:>100k'] = np.where((df_inputs_prepr['revol_bal'] > 100000.), 1, 0)

df_inputs_prepr = df_inputs_prepr.copy()

df_inputs_prepr = df_inputs_prepr.drop(columns = ['revol_bal_factor'])
# Drop the provisory feature

# unique values
df_inputs_prepr['total_bal_il'].unique()

array([     0.,  61045.,   7321., ...,  72775., 108273.,    919.])

# number of observations with 0 value
df_inputs_prepr['total_bal_il'].value_counts()[0]

173265

# 'revol_bal'
df_inputs_prepr['total_bal_il_factor'] = pd.cut(df_inputs_prepr['total_bal_il'], 50)
# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.

# 'revol_bal'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'total_bal_il_factor', df_targets_prepr)
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# one category will be created for 'total_bal_il' = 0 with a count value = 696066.
# one other category will be created for 'total_bal_il' > 200000.
#***********************************************************************************************
# the categories of everyone with 'total_bal_il' different of 0 and less than 200000.
df_inputs_prepr_temp = df_inputs_prepr.loc[(df_inputs_prepr['total_bal_il'] != 0) & (df_inputs_prepr['total_bal_il'] <= 200000), : ]

#df_inputs_prepr_temp
df_inputs_prepr_temp['total_bal_il_factor'] = pd.cut(df_inputs_prepr_temp['total_bal_il'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'total_bal_il_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1452105147.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['total_bal_il_factor'] = pd.cut(df_inputs_prepr_temp['total_bal_il'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '= 0', '0 - 18000', '18000 - 30000', '30000 - 70000', '70000 - 200000', '> 200000'
df_inputs_prepr['total_bal_il:=0'] = np.where((df_inputs_prepr['total_bal_il'] == 0.), 1, 0)
df_inputs_prepr['total_bal_il:0-18k'] = np.where((df_inputs_prepr['total_bal_il'] > 0.) & (df_inputs_prepr['total_bal_il'] <= 18000.), 1, 0)
df_inputs_prepr['total_bal_il:18-30k'] = np.where((df_inputs_prepr['total_bal_il'] > 18000.) & (df_inputs_prepr['total_bal_il'] <= 30000.), 1, 0)
df_inputs_prepr['total_bal_il:30-70k'] = np.where((df_inputs_prepr['total_bal_il'] > 30000.) & (df_inputs_prepr['total_bal_il'] <= 70000.), 1, 0)
df_inputs_prepr['total_bal_il:70-200k'] = np.where((df_inputs_prepr['total_bal_il'] > 70000.) & (df_inputs_prepr['total_bal_il'] <= 200000.), 1, 0)
df_inputs_prepr['total_bal_il:>200k'] = np.where((df_inputs_prepr['total_bal_il'] > 200000.), 1, 0)

df_inputs_prepr = df_inputs_prepr.drop(columns = ['total_bal_il_factor'])
# Drop the provisory feature

# unique values
df_inputs_prepr['max_bal_bc'].unique()

array([    0., 11140.,  3179., ..., 26142., 15475., 24378.])

# number of observations with 0 value
df_inputs_prepr['max_bal_bc'].value_counts()[0]

164273

# one category will be created for 'max_bal_bc' = 0 with a count value = 660128.
# one other category will be created for 'max_bal_bc' > 50000.
#********************************
# 'max_bal_bc'
# the categories of everyone with 'total_bal_il' different of 0.
df_inputs_prepr_temp = df_inputs_prepr.loc[(df_inputs_prepr['max_bal_bc'] != 0) & (df_inputs_prepr['max_bal_bc'] <= 50000), : ]
# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.

#df_inputs_prepr_temp
df_inputs_prepr_temp['max_bal_bc_factor'] = pd.cut(df_inputs_prepr_temp['max_bal_bc'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'max_bal_bc_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3384841133.py:10: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['max_bal_bc_factor'] = pd.cut(df_inputs_prepr_temp['max_bal_bc'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '= 0', '0 - 8000', '8000 - 16000', '16000 - 26000', '26000 - 50000', '> 50000'
df_inputs_prepr['max_bal_bc:=0'] = np.where((df_inputs_prepr['max_bal_bc'] == 0.), 1, 0)
df_inputs_prepr['max_bal_bc:0-8k'] = np.where((df_inputs_prepr['max_bal_bc'] > 0.) & (df_inputs_prepr['max_bal_bc'] <= 8000.), 1, 0)
df_inputs_prepr['max_bal_bc:8-16k'] = np.where((df_inputs_prepr['max_bal_bc'] > 8000.) & (df_inputs_prepr['max_bal_bc'] <= 16000.), 1, 0)
df_inputs_prepr['max_bal_bc:16-26k'] = np.where((df_inputs_prepr['max_bal_bc'] > 16000.) & (df_inputs_prepr['max_bal_bc'] <= 26000.), 1, 0)
df_inputs_prepr['max_bal_bc:26-50k'] = np.where((df_inputs_prepr['max_bal_bc'] > 26000.) & (df_inputs_prepr['max_bal_bc'] <= 50000.), 1, 0)
df_inputs_prepr['max_bal_bc:>50k'] = np.where((df_inputs_prepr['max_bal_bc'] > 50000.), 1, 0)

# unique values
df_inputs_prepr['avg_cur_bal'].unique()

array([ 4658.,  7654.,  7645., ..., 58635., 49593., 61961.])

# one other category will be created for 'avg_cur_bal' > 100000.
#********************************
# 'avg_cur_bal'
# the categories of everyone with 'avg_cur_bal' different of 0.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['avg_cur_bal'] <= 100000, : ]
# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.

#df_inputs_prepr_temp
df_inputs_prepr_temp['avg_cur_bal_factor'] = pd.cut(df_inputs_prepr_temp['avg_cur_bal'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'avg_cur_bal_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\712759651.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['avg_cur_bal_factor'] = pd.cut(df_inputs_prepr_temp['avg_cur_bal'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '< 7000', '7000 - 15000', '15000 - 30000', '30000 - 50000', '50000 - 100000', '> 100000'
df_inputs_prepr['avg_cur_bal:0-7k'] = np.where((df_inputs_prepr['avg_cur_bal'] >= 0.) & (df_inputs_prepr['avg_cur_bal'] <= 7000.), 1, 0)
df_inputs_prepr['avg_cur_bal:7-15k'] = np.where((df_inputs_prepr['avg_cur_bal'] > 7000.) & (df_inputs_prepr['avg_cur_bal'] <= 15000.), 1, 0)
df_inputs_prepr['avg_cur_bal:15-30k'] = np.where((df_inputs_prepr['avg_cur_bal'] > 15000.) & (df_inputs_prepr['avg_cur_bal'] <= 30000.), 1, 0)
df_inputs_prepr['avg_cur_bal:30-50k'] = np.where((df_inputs_prepr['avg_cur_bal'] > 30000.) & (df_inputs_prepr['avg_cur_bal'] <= 50000.), 1, 0)
df_inputs_prepr['avg_cur_bal:50-100k'] = np.where((df_inputs_prepr['avg_cur_bal'] > 50000.) & (df_inputs_prepr['avg_cur_bal'] <= 100000.), 1, 0)
df_inputs_prepr['avg_cur_bal:>100k'] = np.where((df_inputs_prepr['avg_cur_bal'] > 100000.), 1, 0)

# unique values
df_inputs_prepr['bc_open_to_buy'].unique()

array([ 1221., 19625.,   207., ..., 37116., 37369., 32213.])

# one other category will be created for 'bc_open_to_buy' > 100000.
#********************************
# 'bc_open_to_buy'
# the categories of everyone with 'bc_open_to_buy' different of 0.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['bc_open_to_buy'] <= 100000, : ]
# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.

#df_inputs_prepr_temp
df_inputs_prepr_temp['bc_open_to_buy_factor'] = pd.cut(df_inputs_prepr_temp['bc_open_to_buy'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'bc_open_to_buy_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\284082808.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['bc_open_to_buy_factor'] = pd.cut(df_inputs_prepr_temp['bc_open_to_buy'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '< 5000', '5000 - 15000', '15000 - 30000', '30000 - 45000', '45000 - 100000', '> 100000'
df_inputs_prepr['bc_open_to_buy:0-5k'] = np.where((df_inputs_prepr['bc_open_to_buy'] >= 0.) & (df_inputs_prepr['bc_open_to_buy'] <= 5000.), 1, 0)
df_inputs_prepr['bc_open_to_buy:5-15k'] = np.where((df_inputs_prepr['bc_open_to_buy'] > 5000.) & (df_inputs_prepr['bc_open_to_buy'] <= 15000.), 1, 0)
df_inputs_prepr['bc_open_to_buy:15-30k'] = np.where((df_inputs_prepr['bc_open_to_buy'] > 15000.) & (df_inputs_prepr['bc_open_to_buy'] <= 30000.), 1, 0)
df_inputs_prepr['bc_open_to_buy:30-50k'] = np.where((df_inputs_prepr['bc_open_to_buy'] > 30000.) & (df_inputs_prepr['bc_open_to_buy'] <= 45000.), 1, 0)
df_inputs_prepr['bc_open_to_buy:50-100k'] = np.where((df_inputs_prepr['bc_open_to_buy'] > 45000.) & (df_inputs_prepr['bc_open_to_buy'] <= 100000.), 1, 0)
df_inputs_prepr['bc_open_to_buy:>100k'] = np.where((df_inputs_prepr['bc_open_to_buy'] > 100000.), 1, 0)

# unique values
df_inputs_prepr['revol_bal_to_bc_limit'].nunique()

238525

# 'revol_bal_to_bc_limit'
df_inputs_prepr['revol_bal_to_bc_limit_factor'] = pd.cut(df_inputs_prepr['revol_bal_to_bc_limit'], 50)
# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.

# 'revol_bal_to_bc_limit'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'revol_bal_to_bc_limit_factor', df_targets_prepr)
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

# one other category will be created for 'revol_bal_to_bc_limit' value > 10.
#********************************
# 'revol_bal_to_bc_limit'
# the categories of everyone with 'revol_bal_to_bc_limit' less or equal 10.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['revol_bal_to_bc_limit'] <= 10, : ]

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
#df_inputs_prepr_temp
df_inputs_prepr_temp['revol_bal_to_bc_limit_factor'] = pd.cut(df_inputs_prepr_temp['revol_bal_to_bc_limit'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'revol_bal_to_bc_limit_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1955018008.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['revol_bal_to_bc_limit_factor'] = pd.cut(df_inputs_prepr_temp['revol_bal_to_bc_limit'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '< 0.6', '0.6 - 1.2', '1.2 - 3.6', '3.6- 5.5', '5.5 - 10.', '> 10.'
df_inputs_prepr['revol_bal_to_bc_limit:0-0.6'] = np.where((df_inputs_prepr['revol_bal_to_bc_limit'] <= 0.6), 1, 0)
df_inputs_prepr['revol_bal_to_bc_limit:0.6-1.2'] = np.where((df_inputs_prepr['revol_bal_to_bc_limit'] > 0.6) & (df_inputs_prepr['revol_bal_to_bc_limit'] <= 1.2), 1, 0)
df_inputs_prepr['revol_bal_to_bc_limit:1.2-3.6'] = np.where((df_inputs_prepr['revol_bal_to_bc_limit'] > 1.2) & (df_inputs_prepr['revol_bal_to_bc_limit'] <= 3.4), 1, 0)
df_inputs_prepr['revol_bal_to_bc_limit:3.6-5.5'] = np.where((df_inputs_prepr['revol_bal_to_bc_limit'] > 3.4) & (df_inputs_prepr['revol_bal_to_bc_limit'] <= 5.5), 1, 0)
df_inputs_prepr['revol_bal_to_bc_limit:5.5-10.'] = np.where((df_inputs_prepr['revol_bal_to_bc_limit'] > 5.5) & (df_inputs_prepr['revol_bal_to_bc_limit'] <= 10.), 1, 0)
df_inputs_prepr['revol_bal_to_bc_limit:>10.'] = np.where((df_inputs_prepr['revol_bal_to_bc_limit'] > 10.), 1, 0)

df_inputs_prepr = df_inputs_prepr.drop(columns = ['revol_bal_to_bc_limit_factor'])
# Drop the provisory feature

# unique values
df_inputs_prepr['revol_bal_to_open_to_buy'].nunique()

260618

# 'revol_bal_to_bc_limit'
df_inputs_prepr['revol_bal_to_open_to_buy_factor'] = pd.cut(df_inputs_prepr['revol_bal_to_open_to_buy'], 50)
# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.

# 'revol_bal_to_bc_limit'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'revol_bal_to_open_to_buy_factor', df_targets_prepr)
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

# one other category will be created for 'revol_bal_to_open_to_buy' value > 100.
#********************************
# 'revol_bal_to_open_to_buy'
# the categories of everyone with 'revol_bal_to_open_to_buy' less or equal 100.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['revol_bal_to_open_to_buy'] <= 100, : ]

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
#df_inputs_prepr_temp
df_inputs_prepr_temp['revol_bal_to_open_to_buy_factor'] = pd.cut(df_inputs_prepr_temp['revol_bal_to_open_to_buy'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'revol_bal_to_open_to_buy_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3664876170.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['revol_bal_to_open_to_buy_factor'] = pd.cut(df_inputs_prepr_temp['revol_bal_to_open_to_buy'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '< 2', '2 - 4', '4 - 20', '20 - 100', '> 100.'
df_inputs_prepr['revol_bal_to_open_to_buy:0-2'] = np.where((df_inputs_prepr['revol_bal_to_open_to_buy'] <= 2.), 1, 0)
df_inputs_prepr['revol_bal_to_open_to_buy:2-4'] = np.where((df_inputs_prepr['revol_bal_to_open_to_buy'] > 2.) & (df_inputs_prepr['revol_bal_to_open_to_buy'] <= 4.), 1, 0)
df_inputs_prepr['revol_bal_to_open_to_buy:4-20'] = np.where((df_inputs_prepr['revol_bal_to_open_to_buy'] > 4.) & (df_inputs_prepr['revol_bal_to_open_to_buy'] <= 20.), 1, 0)
df_inputs_prepr['revol_bal_to_open_to_buy:20-100'] = np.where((df_inputs_prepr['revol_bal_to_open_to_buy'] > 20.) & (df_inputs_prepr['revol_bal_to_open_to_buy'] <= 100.), 1, 0)
df_inputs_prepr['revol_bal_to_open_to_buy:>100'] = np.where((df_inputs_prepr['revol_bal_to_open_to_buy'] > 100.), 1, 0)

df_inputs_prepr = df_inputs_prepr.drop(columns = ['revol_bal_to_open_to_buy_factor'])
# Drop the provisory feature

# unique values
df_inputs_prepr['total_bal_ex_mort_to_inc'].max()

102819.0

# one other category will be created for 'total_bal_ex_mort_to_inc' value > 100.
#********************************
# 'total_bal_ex_mort_to_inc'
# the categories of everyone with 'total_bal_ex_mort_to_inc' less or equal 100.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['total_bal_ex_mort_to_inc'] <= 10, : ]

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
#df_inputs_prepr_temp
df_inputs_prepr_temp['total_bal_ex_mort_to_inc_factor'] = pd.cut(df_inputs_prepr_temp['total_bal_ex_mort_to_inc'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'total_bal_ex_mort_to_inc_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3588619369.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['total_bal_ex_mort_to_inc_factor'] = pd.cut(df_inputs_prepr_temp['total_bal_ex_mort_to_inc'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '< 0.4', '0.4 - 1', '1 - 2.6', '2.6 - 4.4', '> 4.4'
df_inputs_prepr['total_bal_ex_mort_to_inc:0-0.4'] = np.where((df_inputs_prepr['total_bal_ex_mort_to_inc'] <= 0.4), 1, 0)
df_inputs_prepr['total_bal_ex_mort_to_inc:0.4-1'] = np.where((df_inputs_prepr['total_bal_ex_mort_to_inc'] > 0.4) & (df_inputs_prepr['total_bal_ex_mort_to_inc'] <= 1.), 1, 0)
df_inputs_prepr['total_bal_ex_mort_to_inc:1-2.6'] = np.where((df_inputs_prepr['total_bal_ex_mort_to_inc'] > 1.) & (df_inputs_prepr['total_bal_ex_mort_to_inc'] <= 2.6), 1, 0)
df_inputs_prepr['total_bal_ex_mort_to_inc:2.6-4.4'] = np.where((df_inputs_prepr['total_bal_ex_mort_to_inc'] > 2.6) & (df_inputs_prepr['total_bal_ex_mort_to_inc'] <= 4.4), 1, 0)
df_inputs_prepr['total_bal_ex_mort_to_inc:>4.4'] = np.where((df_inputs_prepr['total_bal_ex_mort_to_inc'] > 4.4), 1, 0)

# unique values
df_inputs_prepr['total_balance_to_credit_ratio'].nunique()

259985

# 'total_balance_to_credit_ratio'
# the categories of everyone with 'total_balance_to_credit_ratio' less or equal 2.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['total_balance_to_credit_ratio'] <= 2., : ]

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
#df_inputs_prepr_temp
df_inputs_prepr_temp['total_balance_to_credit_ratio_factor'] = pd.cut(df_inputs_prepr_temp['total_balance_to_credit_ratio'], 40)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'total_balance_to_credit_ratio_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1020104122.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['total_balance_to_credit_ratio_factor'] = pd.cut(df_inputs_prepr_temp['total_balance_to_credit_ratio'], 40)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '< 0.05', '0.05 - 0.2', '0.2 - 0.4', '0.4 - 0.7', '0.7 - 1.', '1. - 1.4', '> 1.4'
df_inputs_prepr['total_balance_to_credit_ratio:0-0.05'] = np.where((df_inputs_prepr['total_balance_to_credit_ratio'] <= 0.05), 1, 0)
df_inputs_prepr['total_balance_to_credit_ratio:0.05-0.2'] = np.where((df_inputs_prepr['total_balance_to_credit_ratio'] > 0.05) & (df_inputs_prepr['total_balance_to_credit_ratio'] <= 0.2), 1, 0)
df_inputs_prepr['total_balance_to_credit_ratio:0.2-0.4'] = np.where((df_inputs_prepr['total_balance_to_credit_ratio'] > 0.2) & (df_inputs_prepr['total_balance_to_credit_ratio'] <= 0.4), 1, 0)
df_inputs_prepr['total_balance_to_credit_ratio:0.4-0.7'] = np.where((df_inputs_prepr['total_balance_to_credit_ratio'] > 0.4) & (df_inputs_prepr['total_balance_to_credit_ratio'] <= 0.7), 1, 0)
df_inputs_prepr['total_balance_to_credit_ratio:0.7-1'] = np.where((df_inputs_prepr['total_balance_to_credit_ratio'] > 0.7) & (df_inputs_prepr['total_balance_to_credit_ratio'] <= 1.), 1, 0)
df_inputs_prepr['total_balance_to_credit_ratio:1-1.4'] = np.where((df_inputs_prepr['total_balance_to_credit_ratio'] > 1.) & (df_inputs_prepr['total_balance_to_credit_ratio'] <= 1.4), 1, 0)
df_inputs_prepr['total_balance_to_credit_ratio:>1.4'] = np.where((df_inputs_prepr['total_balance_to_credit_ratio'] > 1.4), 1, 0)

# unique values
df_inputs_prepr['rev_to_il_limit_ratio'].nunique()

215869

# 'rev_to_il_limit_ratio'
# the categories of everyone with 'rev_to_il_limit_ratio' less or equal 2.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['rev_to_il_limit_ratio'] <= 10., : ]

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
#df_inputs_prepr_temp
df_inputs_prepr_temp['rev_to_il_limit_ratio_factor'] = pd.cut(df_inputs_prepr_temp['rev_to_il_limit_ratio'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'rev_to_il_limit_ratio_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\2198525158.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['rev_to_il_limit_ratio_factor'] = pd.cut(df_inputs_prepr_temp['rev_to_il_limit_ratio'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '< 0.6', '0.6 - 0.8', '0.8 - 1.8', '1.8 - 4.5', '4.5 - 10.', '> 10.'
df_inputs_prepr['rev_to_il_limit_ratio:0-0.6'] = np.where((df_inputs_prepr['rev_to_il_limit_ratio'] <= 0.6), 1, 0)
df_inputs_prepr['rev_to_il_limit_ratio:0.6-0.8'] = np.where((df_inputs_prepr['rev_to_il_limit_ratio'] > 0.6) & (df_inputs_prepr['rev_to_il_limit_ratio'] <= 0.8), 1, 0)
df_inputs_prepr['rev_to_il_limit_ratio:0.8-1.8'] = np.where((df_inputs_prepr['rev_to_il_limit_ratio'] > 0.8) & (df_inputs_prepr['rev_to_il_limit_ratio'] <= 1.8), 1, 0)
df_inputs_prepr['rev_to_il_limit_ratio:1.8-4.5'] = np.where((df_inputs_prepr['rev_to_il_limit_ratio'] > 1.8) & (df_inputs_prepr['rev_to_il_limit_ratio'] <= 4.5), 1, 0)
df_inputs_prepr['rev_to_il_limit_ratio:4.5-10'] = np.where((df_inputs_prepr['rev_to_il_limit_ratio'] > 4.5) & (df_inputs_prepr['rev_to_il_limit_ratio'] <= 10.), 1, 0)
df_inputs_prepr['rev_to_il_limit_ratio:>10.'] = np.where((df_inputs_prepr['rev_to_il_limit_ratio'] > 10.), 1, 0)

# unique values
df_inputs_prepr['total_il_high_credit_limit'].nunique()

94012

# 'rev_to_il_limit_ratio'
# the categories of everyone with 'rev_to_il_limit_ratio' less or equal 2.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['total_il_high_credit_limit'] <= 250000., : ]

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
#df_inputs_prepr_temp
df_inputs_prepr_temp['total_il_high_credit_limit_factor'] = pd.cut(df_inputs_prepr_temp['total_il_high_credit_limit'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'total_il_high_credit_limit_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1905072761.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['total_il_high_credit_limit_factor'] = pd.cut(df_inputs_prepr_temp['total_il_high_credit_limit'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '< 5k', '5 - 10k', '10 - 30k', '30 - 35k', '35 - 100k', '> 100k'
df_inputs_prepr['total_il_high_credit_limit:0-5k'] = np.where((df_inputs_prepr['total_il_high_credit_limit'] <= 5000.), 1, 0)
df_inputs_prepr['total_il_high_credit_limit:5-10k'] = np.where((df_inputs_prepr['total_il_high_credit_limit'] > 5000.) & (df_inputs_prepr['total_il_high_credit_limit'] <= 10000.), 1, 0)
df_inputs_prepr['total_il_high_credit_limit:10-30k'] = np.where((df_inputs_prepr['total_il_high_credit_limit'] > 10000.) & (df_inputs_prepr['total_il_high_credit_limit'] <= 30000.), 1, 0)
df_inputs_prepr['total_il_high_credit_limit:30-35k'] = np.where((df_inputs_prepr['total_il_high_credit_limit'] > 30000.) & (df_inputs_prepr['total_il_high_credit_limit'] <= 35000.), 1, 0)
df_inputs_prepr['total_il_high_credit_limit:35-100k'] = np.where((df_inputs_prepr['total_il_high_credit_limit'] > 35000.) & (df_inputs_prepr['total_il_high_credit_limit'] <= 100000.), 1, 0)
df_inputs_prepr['total_il_high_credit_limit:>100k'] = np.where((df_inputs_prepr['total_il_high_credit_limit'] > 100000.), 1, 0)

# unique values
df_inputs_prepr['tot_cur_bal'].nunique()

170012

# 'tot_cur_bal'
# the categories of everyone with 'tot_cur_bal' less or equal 2.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['tot_cur_bal'] <= 500000., : ]

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
#df_inputs_prepr_temp
df_inputs_prepr_temp['tot_cur_bal_factor'] = pd.cut(df_inputs_prepr_temp['tot_cur_bal'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'tot_cur_bal_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1690865902.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['tot_cur_bal_factor'] = pd.cut(df_inputs_prepr_temp['tot_cur_bal'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '< 20k', '20 - 70k', '70 - 80k', '80 - 130k', '130 - 200k', '200 - 250k', '250 - 500k', '> 500k'
df_inputs_prepr['tot_cur_bal:0-20k'] = np.where((df_inputs_prepr['tot_cur_bal'] <= 20000.), 1, 0)
df_inputs_prepr['tot_cur_bal:20-70k'] = np.where((df_inputs_prepr['tot_cur_bal'] > 20000.) & (df_inputs_prepr['tot_cur_bal'] <= 70000.), 1, 0)
df_inputs_prepr['tot_cur_bal:70-80k'] = np.where((df_inputs_prepr['tot_cur_bal'] > 70000.) & (df_inputs_prepr['tot_cur_bal'] <= 80000.), 1, 0)
df_inputs_prepr['tot_cur_bal:80-130k'] = np.where((df_inputs_prepr['tot_cur_bal'] > 80000.) & (df_inputs_prepr['tot_cur_bal'] <= 130000.), 1, 0)
df_inputs_prepr['tot_cur_bal:130-200k'] = np.where((df_inputs_prepr['tot_cur_bal'] > 130000.) & (df_inputs_prepr['tot_cur_bal'] <= 200000.), 1, 0)
df_inputs_prepr['tot_cur_bal:200-250k'] = np.where((df_inputs_prepr['tot_cur_bal'] > 200000.) & (df_inputs_prepr['tot_cur_bal'] <= 250000.), 1, 0)
df_inputs_prepr['tot_cur_bal:250-500k'] = np.where((df_inputs_prepr['tot_cur_bal'] > 250000.) & (df_inputs_prepr['tot_cur_bal'] <= 500000.), 1, 0)
df_inputs_prepr['tot_cur_bal:>500k'] = np.where((df_inputs_prepr['tot_cur_bal'] > 500000.), 1, 0)

# unique values
df_inputs_prepr['open_act_il'].unique()

array([ 0.,  4.,  1.,  5.,  2.,  3.,  8.,  9.,  7., 12.,  6., 10., 23.,
       14., 15., 19., 16., 11., 18., 21., 13., 17., 22., 31., 27., 30.,
       20., 25., 26., 24., 32., 35., 42., 29., 28., 53., 36., 45., 40.,
       34., 37., 33.])

# 'open_act_il'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'open_act_il', df_targets_prepr)

# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '0', '1-5', '6-15', '>=16'
df_inputs_prepr['open_act_il:0'] = np.where((df_inputs_prepr['open_act_il'] == 0), 1, 0)
df_inputs_prepr['open_act_il:1-5'] = np.where((df_inputs_prepr['open_act_il'] >= 1) & (df_inputs_prepr['open_act_il'] <= 5), 1, 0)
df_inputs_prepr['open_act_il:6-15'] = np.where((df_inputs_prepr['open_act_il'] >= 6) & (df_inputs_prepr['open_act_il'] <= 15), 1, 0)
df_inputs_prepr['open_act_il:>=16'] = np.where((df_inputs_prepr['open_act_il'] >= 16), 1, 0)

# unique values
df_inputs_prepr['open_il_12m'].unique()

array([ 0.,  4.,  1.,  3.,  2.,  5.,  6.,  8.,  9.,  7., 11., 10., 20.,
       13., 15.])

# 'open_il_12m'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'open_il_12m', df_targets_prepr)

# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '0', '1-5', '>=6'
df_inputs_prepr['open_il_12m:0'] = np.where((df_inputs_prepr['open_il_12m'] == 0), 1, 0)
df_inputs_prepr['open_il_12m:1-5'] = np.where((df_inputs_prepr['open_il_12m'] >= 1) & (df_inputs_prepr['open_il_12m'] <= 5), 1, 0)
df_inputs_prepr['open_il_12m:>=6'] = np.where((df_inputs_prepr['open_il_12m'] >= 6), 1, 0)

# unique values
df_inputs_prepr['num_actv_rev_tl'].unique()

array([11.,  8.,  5.,  4.,  7., 12.,  0., 13.,  6., 10.,  2.,  3.,  9.,
        1., 20., 14., 23., 16., 19., 15., 17., 25., 22., 18., 24., 26.,
       21., 29., 28., 30., 32., 42., 27., 31., 33., 34., 39., 36., 43.,
       38., 40., 35., 37., 52.])

# 'open_il_12m'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'num_actv_rev_tl', df_targets_prepr)

# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '0', '1-5', '6-9', '10-13', '14-17', '18-26', '>=27' 
# '>=27' will be the reference category
df_inputs_prepr['num_actv_rev_tl:0'] = np.where(df_inputs_prepr['num_actv_rev_tl'].isin([0]), 1, 0)
df_inputs_prepr['num_actv_rev_tl:1-5'] = np.where(df_inputs_prepr['num_actv_rev_tl'].isin(range(1, 6)), 1, 0)
df_inputs_prepr['num_actv_rev_tl:6-9'] = np.where(df_inputs_prepr['num_actv_rev_tl'].isin(range(7, 10)), 1, 0)
df_inputs_prepr['num_actv_rev_tl:10-13'] = np.where(df_inputs_prepr['num_actv_rev_tl'].isin(range(11, 14)), 1, 0)
df_inputs_prepr['num_actv_rev_tl:14-17'] = np.where(df_inputs_prepr['num_actv_rev_tl'].isin(range(14, 18)), 1, 0)
df_inputs_prepr['num_actv_rev_tl:18-26'] = np.where(df_inputs_prepr['num_actv_rev_tl'].isin(range(18, 27)), 1, 0)
df_inputs_prepr['num_actv_rev_tl:>=27'] = np.where(df_inputs_prepr['num_actv_rev_tl'].isin(range(27, 500)), 1, 0)

# unique values
df_inputs_prepr['open_rv_12m'].unique()

array([ 0.,  2.,  3.,  1.,  5.,  6.,  4., 10.,  7.,  8.,  9., 11., 12.,
       14., 13., 15., 16., 18., 28., 22., 21., 17.])

# 'open_rv_12m'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'open_rv_12m', df_targets_prepr)

# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '0', '1-2', '3-5', '6-8', '9-13', '>=14' 
# '>=14' will be the reference category
df_inputs_prepr['open_rv_12m:0'] = np.where(df_inputs_prepr['open_rv_12m'].isin([0]), 1, 0)
df_inputs_prepr['open_rv_12m:1-2'] = np.where(df_inputs_prepr['open_rv_12m'].isin(range(1, 3)), 1, 0)
df_inputs_prepr['open_rv_12m:3-5'] = np.where(df_inputs_prepr['open_rv_12m'].isin(range(3, 6)), 1, 0)
df_inputs_prepr['open_rv_12m:6-8'] = np.where(df_inputs_prepr['open_rv_12m'].isin(range(6, 9)), 1, 0)
df_inputs_prepr['open_rv_12m:9-13'] = np.where(df_inputs_prepr['open_rv_12m'].isin(range(9, 14)), 1, 0)
df_inputs_prepr['open_rv_12m:>=14'] = np.where(df_inputs_prepr['open_rv_12m'].isin(range(14, 500)), 1, 0)

# unique values
df_inputs_prepr['num_bc_tl'].unique()

array([14.,  8.,  3.,  5., 11.,  6., 18.,  0.,  4., 13.,  7.,  9., 19.,
       10., 15., 12., 16.,  2., 17.,  1., 37., 22., 23., 29., 26., 27.,
       20., 28., 21., 33., 36., 24., 49., 34., 25., 35., 31., 32., 38.,
       39., 30., 42., 47., 41., 44., 66., 54., 43., 40., 53., 45., 51.,
       46., 48., 56., 61., 60.])

# 'num_bc_tl'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'num_bc_tl', df_targets_prepr)

# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '0', '1-5', '6-10', '11-20', '21-32', '>=33' 
# '>=33' will be the reference category
df_inputs_prepr['num_bc_tl:0'] = np.where(df_inputs_prepr['num_bc_tl'].isin([0]), 1, 0)
df_inputs_prepr['num_bc_tl:1-5'] = np.where(df_inputs_prepr['num_bc_tl'].isin(range(1, 6)), 1, 0)
df_inputs_prepr['num_bc_tl:6-10'] = np.where(df_inputs_prepr['num_bc_tl'].isin(range(6, 11)), 1, 0)
df_inputs_prepr['num_bc_tl:11-20'] = np.where(df_inputs_prepr['num_bc_tl'].isin(range(11, 21)), 1, 0)
df_inputs_prepr['num_bc_tl:21-32'] = np.where(df_inputs_prepr['num_bc_tl'].isin(range(21, 33)), 1, 0)
df_inputs_prepr['num_bc_tl:>=33'] = np.where(df_inputs_prepr['num_bc_tl'].isin(range(33, 500)), 1, 0)

# unique values
df_inputs_prepr['open_acc_6m'].unique()

array([ 0.,  3.,  1.,  2.,  4., 12.,  5.,  6.,  9.,  7.,  8., 11., 14.,
       10., 15.])

# 'open_acc_6m'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'open_acc_6m', df_targets_prepr)

# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '0', '1-3', '4-7', '>=8' 
# '>=8' will be the reference category
df_inputs_prepr['open_acc_6m:0'] = np.where(df_inputs_prepr['open_acc_6m'].isin([0]), 1, 0)
df_inputs_prepr['open_acc_6m:1-3'] = np.where(df_inputs_prepr['open_acc_6m'].isin(range(1, 4)), 1, 0)
df_inputs_prepr['open_acc_6m:4-7'] = np.where(df_inputs_prepr['open_acc_6m'].isin(range(4, 8)), 1, 0)
df_inputs_prepr['open_acc_6m:>=8'] = np.where(df_inputs_prepr['open_acc_6m'].isin(range(8, 500)), 1, 0)

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1521767103.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['open_acc_6m:>=8'] = np.where(df_inputs_prepr['open_acc_6m'].isin(range(8, 500)), 1, 0)

# unique values
df_inputs_prepr['acc_open_past_24mths'].unique()

array([ 4.,  9.,  8.,  2., 10.,  3.,  5., 13.,  6.,  7.,  0.,  1., 12.,
       17., 11., 16., 14., 15., 18., 19., 20., 27., 29., 21., 22., 26.,
       25., 24., 23., 31., 33., 34., 30., 32., 28., 39., 40., 36., 41.,
       35., 38., 42., 46.])

# 'acc_open_past_24mths'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'acc_open_past_24mths', df_targets_prepr)

# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '0-3', '4-7', '8-13', '14-21', '>=22' 
# '>=22' will be the reference category
df_inputs_prepr['acc_open_past_24mths:0-3'] = np.where(df_inputs_prepr['acc_open_past_24mths'].isin(range(0, 4)), 1, 0)
df_inputs_prepr['acc_open_past_24mths:4-7'] = np.where(df_inputs_prepr['acc_open_past_24mths'].isin(range(4, 8)), 1, 0)
df_inputs_prepr['acc_open_past_24mths:8-13'] = np.where(df_inputs_prepr['acc_open_past_24mths'].isin(range(8, 14)), 1, 0)
df_inputs_prepr['acc_open_past_24mths:14-21'] = np.where(df_inputs_prepr['acc_open_past_24mths'].isin(range(14, 22)), 1, 0)
df_inputs_prepr['acc_open_past_24mths:>=22'] = np.where(df_inputs_prepr['acc_open_past_24mths'].isin(range(22, 500)), 1, 0)

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1260450660.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['acc_open_past_24mths:0-3'] = np.where(df_inputs_prepr['acc_open_past_24mths'].isin(range(0, 4)), 1, 0)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1260450660.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['acc_open_past_24mths:4-7'] = np.where(df_inputs_prepr['acc_open_past_24mths'].isin(range(4, 8)), 1, 0)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1260450660.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['acc_open_past_24mths:8-13'] = np.where(df_inputs_prepr['acc_open_past_24mths'].isin(range(8, 14)), 1, 0)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1260450660.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['acc_open_past_24mths:14-21'] = np.where(df_inputs_prepr['acc_open_past_24mths'].isin(range(14, 22)), 1, 0)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1260450660.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['acc_open_past_24mths:>=22'] = np.where(df_inputs_prepr['acc_open_past_24mths'].isin(range(22, 500)), 1, 0)

# unique values
df_inputs_prepr['total_cu_tl'].unique()

array([ 0.,  2.,  1.,  8.,  4.,  3.,  5.,  6., 10., 11., 22.,  9.,  7.,
       12., 17., 13., 15., 24., 14., 19., 16., 31., 23., 20., 21., 18.,
       27., 28., 33., 26., 38., 34., 25., 29., 48., 37., 32., 43., 30.,
       40., 41.])

# 'total_cu_tl'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'total_cu_tl', df_targets_prepr)

# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '0', '1-7', '8-17', '>=18' 
# '>=18' will be the reference category
df_inputs_prepr['total_cu_tl:0'] = np.where(df_inputs_prepr['total_cu_tl'].isin([0]), 1, 0)
df_inputs_prepr['total_cu_tl:1-7'] = np.where(df_inputs_prepr['total_cu_tl'].isin(range(1, 8)), 1, 0)
df_inputs_prepr['total_cu_tl:8-17'] = np.where(df_inputs_prepr['total_cu_tl'].isin(range(8, 18)), 1, 0)
df_inputs_prepr['total_cu_tl:>=18'] = np.where(df_inputs_prepr['total_cu_tl'].isin(range(18, 500)), 1, 0)

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3794626681.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['total_cu_tl:0'] = np.where(df_inputs_prepr['total_cu_tl'].isin([0]), 1, 0)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3794626681.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['total_cu_tl:1-7'] = np.where(df_inputs_prepr['total_cu_tl'].isin(range(1, 8)), 1, 0)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3794626681.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['total_cu_tl:8-17'] = np.where(df_inputs_prepr['total_cu_tl'].isin(range(8, 18)), 1, 0)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3794626681.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['total_cu_tl:>=18'] = np.where(df_inputs_prepr['total_cu_tl'].isin(range(18, 500)), 1, 0)

# unique values
df_inputs_prepr['inq_last_12m'].unique()

array([ 0.,  4.,  2.,  3.,  1.,  5.,  7., 12.,  6., 14., 11., 18.,  8.,
       20., 10., 15.,  9., 16., 28., 13., 21., 17., 22., 19., 32., 26.,
       23., 25., 29., 24., 33., 34., 31., 30., 27., 40.])

# 'inq_last_12m'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'inq_last_12m', df_targets_prepr)

# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '0', '1-4', '5-9', '10-16', '>=17' 
# '>=17' will be the reference category
df_inputs_prepr['inq_last_12m:0'] = np.where(df_inputs_prepr['inq_last_12m'].isin([0]), 1, 0)
df_inputs_prepr['inq_last_12m:1-4'] = np.where(df_inputs_prepr['inq_last_12m'].isin(range(1, 5)), 1, 0)
df_inputs_prepr['inq_last_12m:5-9'] = np.where(df_inputs_prepr['inq_last_12m'].isin(range(5, 10)), 1, 0)
df_inputs_prepr['inq_last_12m:10-16'] = np.where(df_inputs_prepr['inq_last_12m'].isin(range(10, 17)), 1, 0)
df_inputs_prepr['inq_last_12m:>=17'] = np.where(df_inputs_prepr['inq_last_12m'].isin(range(17, 500)), 1, 0)

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\2586074654.py:3: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['inq_last_12m:0'] = np.where(df_inputs_prepr['inq_last_12m'].isin([0]), 1, 0)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\2586074654.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['inq_last_12m:1-4'] = np.where(df_inputs_prepr['inq_last_12m'].isin(range(1, 5)), 1, 0)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\2586074654.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['inq_last_12m:5-9'] = np.where(df_inputs_prepr['inq_last_12m'].isin(range(5, 10)), 1, 0)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\2586074654.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['inq_last_12m:10-16'] = np.where(df_inputs_prepr['inq_last_12m'].isin(range(10, 17)), 1, 0)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\2586074654.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr['inq_last_12m:>=17'] = np.where(df_inputs_prepr['inq_last_12m'].isin(range(17, 500)), 1, 0)

# unique values
df_inputs_prepr['mths_since_recent_inq'].unique()

array([  5.,   3.,  21.,   0.,   1.,  19.,   2.,  11.,   4., 999.,  12.,
         7.,   9.,  10.,  15.,  14.,  20.,   8.,   6.,  18.,  22.,  13.,
        16.,  23.,  17.,  24.,  25.])

# 'mths_since_recent_inq'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'mths_since_recent_inq', df_targets_prepr)

# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '0-1', '2-3', '4-6', '7-10', '11-15', '>=16, 'Missing' = 999.
# '>=17' will be the reference category
df_inputs_prepr['mths_since_recent_inq:Missing'] = np.where(df_inputs_prepr['mths_since_recent_inq'].isin([999]), 1, 0)
df_inputs_prepr['mths_since_recent_inq:0-1'] = np.where(df_inputs_prepr['mths_since_recent_inq'].isin(range(0, 2)), 1, 0)
df_inputs_prepr['mths_since_recent_inq:2-3'] = np.where(df_inputs_prepr['mths_since_recent_inq'].isin(range(2, 4)), 1, 0)
df_inputs_prepr['mths_since_recent_inq:4-6'] = np.where(df_inputs_prepr['mths_since_recent_inq'].isin(range(4, 7)), 1, 0)
df_inputs_prepr['mths_since_recent_inq:7-10'] = np.where(df_inputs_prepr['mths_since_recent_inq'].isin(range(7, 11)), 1, 0)
df_inputs_prepr['mths_since_recent_inq:11-15'] = np.where(df_inputs_prepr['mths_since_recent_inq'].isin(range(11, 16)), 1, 0)
df_inputs_prepr['mths_since_recent_inq:>=16'] = np.where(df_inputs_prepr['mths_since_recent_inq'].isin(range(16, 500)), 1, 0)

# unique values
df_inputs_prepr['out_prncp'].unique()

array([    0.  , 17331.34,   357.91, ..., 13382.28, 24725.78, 29443.26])

df_inputs_prepr.loc[df_inputs_prepr['out_prncp'] == 0, : ]['out_prncp'].count()

269105

# one other category will be created for 'out_prncp' value = 0.
#********************************
# 'out_prncp'
# the categories of everyone with 'out_prncp' less or equal 100.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['out_prncp'] != 0, : ]

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
#df_inputs_prepr_temp
df_inputs_prepr_temp['out_prncp_factor'] = pd.cut(df_inputs_prepr_temp['out_prncp'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'out_prncp_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\469751821.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr_temp['out_prncp_factor'] = pd.cut(df_inputs_prepr_temp['out_prncp'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\469751821.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['out_prncp_factor'] = pd.cut(df_inputs_prepr_temp['out_prncp'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp.iloc[: 50, : ], 90)
# We plot the weight of evidence values.

# Categories: '=0', '>0'
df_inputs_prepr['out_prncp:=0'] = np.where((df_inputs_prepr['out_prncp'] == 0.), 1, 0)
df_inputs_prepr['out_prncp:>0'] = np.where((df_inputs_prepr['out_prncp'] > 0.), 1, 0)

# unique values
df_inputs_prepr['last_pymnt_amnt'].nunique()

193962

df_inputs_prepr['last_pymnt_amnt'].max()

42148.53

# one other category will be created for 'last_pymnt_amnt_factor' value > 10000.
#********************************
# 'last_pymnt_amnt_factor'
# the categories of everyone with 'last_pymnt_amnt_factor' less or equal 10000.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['last_pymnt_amnt'] <= 10000., : ]

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
#df_inputs_prepr_temp
df_inputs_prepr_temp['last_pymnt_amnt_factor'] = pd.cut(df_inputs_prepr_temp['last_pymnt_amnt'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'last_pymnt_amnt_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\462666328.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr_temp['last_pymnt_amnt_factor'] = pd.cut(df_inputs_prepr_temp['last_pymnt_amnt'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\462666328.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['last_pymnt_amnt_factor'] = pd.cut(df_inputs_prepr_temp['last_pymnt_amnt'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

#plot_by_woe(df_temp.iloc[7: , : ], 90)
plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '<=200', '200-700', '700-1000', '1000-1500', '1500-2600', '2600-10K', ' >10K'
df_inputs_prepr['last_pymnt_amnt:<=200'] = np.where((df_inputs_prepr['last_pymnt_amnt'] <= 200), 1, 0)
df_inputs_prepr['last_pymnt_amnt:200-700'] = np.where((df_inputs_prepr['last_pymnt_amnt'] > 200) & (df_inputs_prepr['last_pymnt_amnt'] <= 700), 1, 0)
df_inputs_prepr['last_pymnt_amnt:700-1000'] = np.where((df_inputs_prepr['last_pymnt_amnt'] > 700) & (df_inputs_prepr['last_pymnt_amnt'] <= 1000), 1, 0)
df_inputs_prepr['last_pymnt_amnt:1000-1500'] = np.where((df_inputs_prepr['last_pymnt_amnt'] > 1000) & (df_inputs_prepr['last_pymnt_amnt'] <= 1500), 1, 0)
df_inputs_prepr['last_pymnt_amnt:1500-2600'] = np.where((df_inputs_prepr['last_pymnt_amnt'] > 1500) & (df_inputs_prepr['last_pymnt_amnt'] <= 2600), 1, 0)
df_inputs_prepr['last_pymnt_amnt:2600-10000'] = np.where((df_inputs_prepr['last_pymnt_amnt'] > 2600) & (df_inputs_prepr['last_pymnt_amnt'] <= 10000), 1, 0)
df_inputs_prepr['last_pymnt_amnt:>10000'] = np.where((df_inputs_prepr['last_pymnt_amnt'] > 10000), 1, 0)

df_inputs_prepr['principal_paid_ratio'].nunique()

54695

df_inputs_prepr.loc[df_inputs_prepr['principal_paid_ratio'] >= 1., : ]['principal_paid_ratio'].count()

214732

# one other category will be created for 'principal_paid_ratio' value = 1 with a count of 858698.
#********************************
# 'principal_paid_ratio'
# the categories of everyone with 'principal_paid_ratio' < 1.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['principal_paid_ratio'] < 1., : ]

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
#df_inputs_prepr_temp
df_inputs_prepr_temp['principal_paid_ratio_factor'] = pd.cut(df_inputs_prepr_temp['principal_paid_ratio'], 20)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'principal_paid_ratio_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1212135044.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr_temp['principal_paid_ratio_factor'] = pd.cut(df_inputs_prepr_temp['principal_paid_ratio'], 20)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1212135044.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['principal_paid_ratio_factor'] = pd.cut(df_inputs_prepr_temp['principal_paid_ratio'], 20)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# Categories: '<= 0.3', '0.3 - 0.45', '0.45 - 0.6', '0.6 - 1', ' = 1'
df_inputs_prepr['principal_paid_ratio:<=0.3'] = np.where((df_inputs_prepr['principal_paid_ratio'] <= 0.3), 1, 0)
df_inputs_prepr['principal_paid_ratio:0.3-0.45'] = np.where((df_inputs_prepr['principal_paid_ratio'] > 0.3) & (df_inputs_prepr['principal_paid_ratio'] <= 0.45), 1, 0)
df_inputs_prepr['principal_paid_ratio:0.45-0.6'] = np.where((df_inputs_prepr['principal_paid_ratio'] > 0.45) & (df_inputs_prepr['principal_paid_ratio'] <= 0.6), 1, 0)
df_inputs_prepr['principal_paid_ratio:0.6-1'] = np.where((df_inputs_prepr['principal_paid_ratio'] > 0.6) & (df_inputs_prepr['principal_paid_ratio'] <= 1.), 1, 0)
df_inputs_prepr['principal_paid_ratio:=1'] = np.where((df_inputs_prepr['principal_paid_ratio'] >= 1.), 1, 0)

df_inputs_prepr['fico_range_high'].unique()

array([679., 699., 664., 709., 684., 704., 714., 674., 739., 754., 694.,
       749., 779., 669., 784., 689., 719., 724., 729., 819., 794., 769.,
       774., 734., 744., 789., 759., 764., 809., 814., 799., 824., 834.,
       804., 829., 844., 839., 850.])

# 'fico_range_high'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'fico_range_high', df_targets_prepr)

# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp.iloc[2: , : ], 90)
#plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '< 680', '680-700', '700-720', '720-750', '750-795', '> 795.
# '> 795' will be the reference category
df_inputs_prepr['fico_range_high:<=680'] = np.where((df_inputs_prepr['fico_range_high'] <= 680), 1, 0)
df_inputs_prepr['fico_range_high:680-700'] = np.where((df_inputs_prepr['fico_range_high'] > 680) & (df_inputs_prepr['fico_range_high'] <= 700), 1, 0)
df_inputs_prepr['fico_range_high:700-720'] = np.where((df_inputs_prepr['fico_range_high'] > 700) & (df_inputs_prepr['fico_range_high'] <= 720), 1, 0)
df_inputs_prepr['fico_range_high:720-750'] = np.where((df_inputs_prepr['fico_range_high'] > 720) & (df_inputs_prepr['fico_range_high'] <= 750), 1, 0)
df_inputs_prepr['fico_range_high:750-795'] = np.where((df_inputs_prepr['fico_range_high'] > 750) & (df_inputs_prepr['fico_range_high'] <= 795), 1, 0)
df_inputs_prepr['fico_range_high:>795'] = np.where((df_inputs_prepr['fico_range_high'] > 795), 1, 0)

df_inputs_prepr['last_fico_range_high'].unique()

array([574., 709., 714., 749., 664., 799., 589., 689., 499., 694., 579.,
       784., 774., 669., 554., 684., 529., 659., 734., 769., 739., 569.,
       834., 634., 594., 674., 614., 724., 789., 719., 729., 704., 544.,
       629., 584., 524., 599., 509., 699., 779., 619., 549., 744., 654.,
       649., 794., 754., 539., 624., 804., 759., 604., 519., 534., 679.,
       764., 644., 819., 609., 839., 559., 829., 639., 504., 564., 809.,
       814., 824., 514.,   0., 844., 850.])

# 'fico_range_high'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'last_fico_range_high', df_targets_prepr)

# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp.iloc[1: 50, : ], 90)
#plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '< 520', '520-550', '550-580', '580-610', '610-640', '640-670', '> 670.
# '> 670' will be the reference category
df_inputs_prepr['last_fico_range_high:<=520'] = np.where((df_inputs_prepr['last_fico_range_high'] <= 520), 1, 0)
df_inputs_prepr['last_fico_range_high:520-550'] = np.where((df_inputs_prepr['last_fico_range_high'] > 520) & (df_inputs_prepr['last_fico_range_high'] <= 550), 1, 0)
df_inputs_prepr['last_fico_range_high:550-580'] = np.where((df_inputs_prepr['last_fico_range_high'] > 550) & (df_inputs_prepr['last_fico_range_high'] <= 580), 1, 0)
df_inputs_prepr['last_fico_range_high:580-610'] = np.where((df_inputs_prepr['last_fico_range_high'] > 580) & (df_inputs_prepr['last_fico_range_high'] <= 610), 1, 0)
df_inputs_prepr['last_fico_range_high:610-640'] = np.where((df_inputs_prepr['last_fico_range_high'] > 610) & (df_inputs_prepr['last_fico_range_high'] <= 640), 1, 0)
df_inputs_prepr['last_fico_range_high:640-670'] = np.where((df_inputs_prepr['last_fico_range_high'] > 640) & (df_inputs_prepr['last_fico_range_high'] <= 670), 1, 0)
df_inputs_prepr['last_fico_range_high:>670'] = np.where((df_inputs_prepr['last_fico_range_high'] > 670), 1, 0)

df_inputs_prepr['mo_sin_rcnt_rev_tl_op'].nunique()

224

# 'mo_sin_rcnt_rev_tl_op'
# the categories of everyone with 'mo_sin_rcnt_rev_tl_op' less or equal 140.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['mo_sin_rcnt_rev_tl_op'] <= 150., : ]

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
#df_inputs_prepr_temp
df_inputs_prepr_temp['mo_sin_rcnt_rev_tl_op_factor'] = pd.cut(df_inputs_prepr_temp['mo_sin_rcnt_rev_tl_op'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'mo_sin_rcnt_rev_tl_op_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3692458730.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr_temp['mo_sin_rcnt_rev_tl_op_factor'] = pd.cut(df_inputs_prepr_temp['mo_sin_rcnt_rev_tl_op'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3692458730.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['mo_sin_rcnt_rev_tl_op_factor'] = pd.cut(df_inputs_prepr_temp['mo_sin_rcnt_rev_tl_op'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp.iloc[13 : 47, : ], 90)
#plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '0-3', '3-6', '6-9', '9-20', '20-37', '37-63', '63-80', '80-140', '> 140'.
# '> 140' will be the reference category
df_inputs_prepr['mo_sin_rcnt_rev_tl_op:0-3'] = np.where((df_inputs_prepr['mo_sin_rcnt_rev_tl_op'] <= 3), 1, 0)
df_inputs_prepr['mo_sin_rcnt_rev_tl_op:3-6'] = np.where((df_inputs_prepr['mo_sin_rcnt_rev_tl_op'] > 3) & (df_inputs_prepr['mo_sin_rcnt_rev_tl_op'] <= 6), 1, 0)
df_inputs_prepr['mo_sin_rcnt_rev_tl_op:6-9'] = np.where((df_inputs_prepr['mo_sin_rcnt_rev_tl_op'] > 6) & (df_inputs_prepr['mo_sin_rcnt_rev_tl_op'] <= 9), 1, 0)
df_inputs_prepr['mo_sin_rcnt_rev_tl_op:9-20'] = np.where((df_inputs_prepr['mo_sin_rcnt_rev_tl_op'] > 9) & (df_inputs_prepr['mo_sin_rcnt_rev_tl_op'] <= 20), 1, 0)
df_inputs_prepr['mo_sin_rcnt_rev_tl_op:20-37'] = np.where((df_inputs_prepr['mo_sin_rcnt_rev_tl_op'] > 20) & (df_inputs_prepr['mo_sin_rcnt_rev_tl_op'] <= 37), 1, 0)
df_inputs_prepr['mo_sin_rcnt_rev_tl_op:37-63'] = np.where((df_inputs_prepr['mo_sin_rcnt_rev_tl_op'] > 37) & (df_inputs_prepr['mo_sin_rcnt_rev_tl_op'] <= 63), 1, 0)
df_inputs_prepr['mo_sin_rcnt_rev_tl_op:63-80'] = np.where((df_inputs_prepr['mo_sin_rcnt_rev_tl_op'] > 63) & (df_inputs_prepr['mo_sin_rcnt_rev_tl_op'] <= 80), 1, 0)
df_inputs_prepr['mo_sin_rcnt_rev_tl_op:80-140'] = np.where((df_inputs_prepr['mo_sin_rcnt_rev_tl_op'] > 80) & (df_inputs_prepr['mo_sin_rcnt_rev_tl_op'] <= 140), 1, 0)
df_inputs_prepr['mo_sin_rcnt_rev_tl_op:>140'] = np.where((df_inputs_prepr['mo_sin_rcnt_rev_tl_op'] > 140), 1, 0)

df_inputs_prepr['mo_sin_rcnt_tl'].nunique()

153

# 'mo_sin_rcnt_tl'
# the categories of everyone with 'mo_sin_rcnt_tl' less or equal 50.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['mo_sin_rcnt_tl'] <= 50, : ]

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
#df_inputs_prepr_temp
df_inputs_prepr_temp['mo_sin_rcnt_tl_factor'] = pd.cut(df_inputs_prepr_temp['mo_sin_rcnt_tl'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'mo_sin_rcnt_tl_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\2033739020.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr_temp['mo_sin_rcnt_tl_factor'] = pd.cut(df_inputs_prepr_temp['mo_sin_rcnt_tl'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\2033739020.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['mo_sin_rcnt_tl_factor'] = pd.cut(df_inputs_prepr_temp['mo_sin_rcnt_tl'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp.iloc[ 8: , : ], 90)
#plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '0-2', '2-5', '5-6', '6-10', '10-15', '15-20', '20-50', '> 50'.
# '> 50' will be the reference category
df_inputs_prepr['mo_sin_rcnt_tl:0-2'] = np.where((df_inputs_prepr['mo_sin_rcnt_tl'] <= 2), 1, 0)
df_inputs_prepr['mo_sin_rcnt_tl:2-5'] = np.where((df_inputs_prepr['mo_sin_rcnt_tl'] > 2) & (df_inputs_prepr['mo_sin_rcnt_tl'] <= 5), 1, 0)
df_inputs_prepr['mo_sin_rcnt_tl:5-6'] = np.where((df_inputs_prepr['mo_sin_rcnt_tl'] > 5) & (df_inputs_prepr['mo_sin_rcnt_tl'] <= 6), 1, 0)
df_inputs_prepr['mo_sin_rcnt_tl:6-10'] = np.where((df_inputs_prepr['mo_sin_rcnt_tl'] > 6) & (df_inputs_prepr['mo_sin_rcnt_tl'] <= 10), 1, 0)
df_inputs_prepr['mo_sin_rcnt_tl:10-15'] = np.where((df_inputs_prepr['mo_sin_rcnt_tl'] > 10) & (df_inputs_prepr['mo_sin_rcnt_tl'] <= 15), 1, 0)
df_inputs_prepr['mo_sin_rcnt_tl:15-20'] = np.where((df_inputs_prepr['mo_sin_rcnt_tl'] > 15) & (df_inputs_prepr['mo_sin_rcnt_tl'] <= 20), 1, 0)
df_inputs_prepr['mo_sin_rcnt_tl:20-50'] = np.where((df_inputs_prepr['mo_sin_rcnt_tl'] > 20) & (df_inputs_prepr['mo_sin_rcnt_tl'] <= 50), 1, 0)
df_inputs_prepr['mo_sin_rcnt_tl:>50'] = np.where((df_inputs_prepr['mo_sin_rcnt_tl'] > 50), 1, 0)

df_inputs_prepr['mths_since_rcnt_il'].nunique()

271

df_inputs_prepr.loc[df_inputs_prepr['mths_since_rcnt_il'] >= 999., : ]['mths_since_rcnt_il'].count()

164353

# 'mths_since_rcnt_il'
# the categories of everyone with 'mths_since_rcnt_il' less or equal 100.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['mths_since_rcnt_il'] <= 100, : ]

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
# df_inputs_prepr_temp
df_inputs_prepr_temp['mths_since_rcnt_il_factor'] = pd.cut(df_inputs_prepr_temp['mths_since_rcnt_il'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'mths_since_rcnt_il_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3212778370.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr_temp['mths_since_rcnt_il_factor'] = pd.cut(df_inputs_prepr_temp['mths_since_rcnt_il'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3212778370.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['mths_since_rcnt_il_factor'] = pd.cut(df_inputs_prepr_temp['mths_since_rcnt_il'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp.iloc[5 : 50, : ], 90)
#plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: 'Missig','0-4', '4-10', '10-20', '20-40', '40-100', '> 100'.
# 'Missing' will be the reference category.
df_inputs_prepr['mths_since_rcnt_il:0-4'] = np.where((df_inputs_prepr['mths_since_rcnt_il'] <= 4), 1, 0)
df_inputs_prepr['mths_since_rcnt_il:4-10'] = np.where((df_inputs_prepr['mths_since_rcnt_il'] > 4) & (df_inputs_prepr['mths_since_rcnt_il'] <= 10), 1, 0)
df_inputs_prepr['mths_since_rcnt_il:10-20'] = np.where((df_inputs_prepr['mths_since_rcnt_il'] > 10) & (df_inputs_prepr['mths_since_rcnt_il'] <= 20), 1, 0)
df_inputs_prepr['mths_since_rcnt_il:20-40'] = np.where((df_inputs_prepr['mths_since_rcnt_il'] > 20) & (df_inputs_prepr['mths_since_rcnt_il'] <= 40), 1, 0)
df_inputs_prepr['mths_since_rcnt_il:40-100'] = np.where((df_inputs_prepr['mths_since_rcnt_il'] > 40) & (df_inputs_prepr['mths_since_rcnt_il'] <= 100), 1, 0)
df_inputs_prepr['mths_since_rcnt_il:>100'] = np.where((df_inputs_prepr['mths_since_rcnt_il'] > 100) & (df_inputs_prepr['mths_since_rcnt_il'] <= 700), 1, 0)
df_inputs_prepr['mths_since_rcnt_il:Missing'] = np.where((df_inputs_prepr['mths_since_rcnt_il'] == 999), 1, 0)

df_inputs_prepr['mths_since_recent_bc'].nunique()

388

# 'mths_since_rcnt_il'
# the categories of everyone with 'mths_since_rcnt_il' less or equal 100.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['mths_since_recent_bc'] <= 200, : ]

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
# df_inputs_prepr_temp
df_inputs_prepr_temp['mths_since_recent_bc_factor'] = pd.cut(df_inputs_prepr_temp['mths_since_recent_bc'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'mths_since_recent_bc_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3184696751.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr_temp['mths_since_recent_bc_factor'] = pd.cut(df_inputs_prepr_temp['mths_since_recent_bc'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3184696751.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['mths_since_recent_bc_factor'] = pd.cut(df_inputs_prepr_temp['mths_since_recent_bc'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp.iloc[10 : , : ], 90)
#plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '0-12', '12-32', '32-52', '52-68', '68-100', '100-130', '> 130'.
# '> 130' will be the reference category.
df_inputs_prepr['mths_since_recent_bc:0-12'] = np.where((df_inputs_prepr['mths_since_recent_bc'] <= 12), 1, 0)
df_inputs_prepr['mths_since_recent_bc:12-32'] = np.where((df_inputs_prepr['mths_since_recent_bc'] > 12) & (df_inputs_prepr['mths_since_recent_bc'] <= 32), 1, 0)
df_inputs_prepr['mths_since_recent_bc:32-52'] = np.where((df_inputs_prepr['mths_since_recent_bc'] > 32) & (df_inputs_prepr['mths_since_recent_bc'] <= 52), 1, 0)
df_inputs_prepr['mths_since_recent_bc:52-68'] = np.where((df_inputs_prepr['mths_since_recent_bc'] > 52) & (df_inputs_prepr['mths_since_recent_bc'] <= 68), 1, 0)
df_inputs_prepr['mths_since_recent_bc:68-100'] = np.where((df_inputs_prepr['mths_since_recent_bc'] > 68) & (df_inputs_prepr['mths_since_recent_bc'] <= 100), 1, 0)
df_inputs_prepr['mths_since_recent_bc:100-130'] = np.where((df_inputs_prepr['mths_since_recent_bc'] > 100) & (df_inputs_prepr['mths_since_recent_bc'] <= 130), 1, 0)
df_inputs_prepr['mths_since_recent_bc:>130'] = np.where((df_inputs_prepr['mths_since_recent_bc'] > 130), 1, 0)

df_inputs_prepr['mths_since_recent_revol_delinq'].nunique()

139

df_inputs_prepr.loc[df_inputs_prepr['mths_since_recent_revol_delinq'] >= 999., : ]['mths_since_recent_revol_delinq'].count()

182457

# 'mths_since_recent_revol_delinq'
# the categories of everyone with 'mths_since_recent_revol_delinq' less or equal 84.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['mths_since_recent_revol_delinq'] <= 120, : ]

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
# df_inputs_prepr_temp
df_inputs_prepr_temp['mths_since_recent_revol_delinq_factor'] = pd.cut(df_inputs_prepr_temp['mths_since_recent_revol_delinq'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'mths_since_recent_revol_delinq_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3078955673.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr_temp['mths_since_recent_revol_delinq_factor'] = pd.cut(df_inputs_prepr_temp['mths_since_recent_revol_delinq'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\3078955673.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['mths_since_recent_revol_delinq_factor'] = pd.cut(df_inputs_prepr_temp['mths_since_recent_revol_delinq'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp.iloc[ : , : ], 90)
#plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '0-20', '20-34', '34-50', '50-84', '>84', 'Missing'.
# '> 84' will be the reference category.
df_inputs_prepr['mths_since_recent_revol_delinq:0-20'] = np.where((df_inputs_prepr['mths_since_recent_revol_delinq'] <= 20), 1, 0)
df_inputs_prepr['mths_since_recent_revol_delinq:20-34'] = np.where((df_inputs_prepr['mths_since_recent_revol_delinq'] > 20) & (df_inputs_prepr['mths_since_recent_revol_delinq'] <= 34), 1, 0)
df_inputs_prepr['mths_since_recent_revol_delinq:34-50'] = np.where((df_inputs_prepr['mths_since_recent_revol_delinq'] > 34) & (df_inputs_prepr['mths_since_recent_revol_delinq'] <= 50), 1, 0)
df_inputs_prepr['mths_since_recent_revol_delinq:50-84'] = np.where((df_inputs_prepr['mths_since_recent_revol_delinq'] > 50) & (df_inputs_prepr['mths_since_recent_revol_delinq'] <= 84), 1, 0)
df_inputs_prepr['mths_since_recent_revol_delinq:>84'] = np.where((df_inputs_prepr['mths_since_recent_revol_delinq'] > 84) & (df_inputs_prepr['mths_since_recent_revol_delinq'] <= 800), 1, 0)
df_inputs_prepr['mths_since_recent_revol_delinq:Missing'] = np.where((df_inputs_prepr['mths_since_recent_revol_delinq'] == 999), 1, 0)

df_inputs_prepr['percent_bc_gt_75'].nunique()

183

# 'percent_bc_gt_75'
# the categories of everyone with 'percent_bc_gt_75' less or equal 100.
df_inputs_prepr_temp = df_inputs_prepr.loc[df_inputs_prepr['percent_bc_gt_75'] <= 300, : ]

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
# df_inputs_prepr_temp
df_inputs_prepr_temp['percent_bc_gt_75_factor'] = pd.cut(df_inputs_prepr_temp['percent_bc_gt_75'], 25)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'percent_bc_gt_75_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\4160733302.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr_temp['percent_bc_gt_75_factor'] = pd.cut(df_inputs_prepr_temp['percent_bc_gt_75'], 25)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp.iloc[ : , : ], 90)
#plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '0-4', '4-20', '20-40', '40-70', '70-96', '>96'.
# '> 84' will be the reference category.
df_inputs_prepr['percent_bc_gt_75:0-4'] = np.where((df_inputs_prepr['percent_bc_gt_75'] <= 4), 1, 0)
df_inputs_prepr['percent_bc_gt_75:4-20'] = np.where((df_inputs_prepr['percent_bc_gt_75'] > 4) & (df_inputs_prepr['percent_bc_gt_75'] <= 20), 1, 0)
df_inputs_prepr['percent_bc_gt_75:20-40'] = np.where((df_inputs_prepr['percent_bc_gt_75'] > 20) & (df_inputs_prepr['percent_bc_gt_75'] <= 40), 1, 0)
df_inputs_prepr['percent_bc_gt_75:40-70'] = np.where((df_inputs_prepr['percent_bc_gt_75'] > 40) & (df_inputs_prepr['percent_bc_gt_75'] <= 70), 1, 0)
df_inputs_prepr['percent_bc_gt_75:70-96'] = np.where((df_inputs_prepr['percent_bc_gt_75'] > 70) & (df_inputs_prepr['percent_bc_gt_75'] <= 96), 1, 0)
df_inputs_prepr['percent_bc_gt_75:>96'] = np.where((df_inputs_prepr['percent_bc_gt_75'] > 96), 1, 0)

df_inputs_prepr['pub_rec_bankruptcies'].unique()

array([ 0.,  1.,  2.,  3.,  6.,  5.,  4.,  7.,  8., 12.])

# 'mths_since_recent_inq'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'pub_rec_bankruptcies', df_targets_prepr)

# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '0', '1-3', '>=4'.
# '>=4' will be the reference category
df_inputs_prepr['pub_rec_bankruptcies:0'] = np.where(df_inputs_prepr['pub_rec_bankruptcies'].isin([0]), 1, 0)
df_inputs_prepr['pub_rec_bankruptcies:1-3'] = np.where(df_inputs_prepr['pub_rec_bankruptcies'].isin(range(1, 4)), 1, 0)
df_inputs_prepr['pub_rec_bankruptcies:>4'] = np.where(df_inputs_prepr['pub_rec_bankruptcies'].isin(range(4, 100)), 1, 0)

df_inputs_prepr['tot_coll_amt'].nunique()

6183

df_inputs_prepr.loc[df_inputs_prepr['tot_coll_amt'] == 0., : ]['tot_coll_amt'].count()

234254

# 'tot_coll_amt'
# the categories of everyone with 'tot_coll_amt' less or equal 100.
df_inputs_prepr_temp = df_inputs_prepr.loc[(df_inputs_prepr['tot_coll_amt'] != 0) & (df_inputs_prepr['tot_coll_amt'] <= 1000), : ]

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
# df_inputs_prepr_temp
df_inputs_prepr_temp['tot_coll_amt_factor'] = pd.cut(df_inputs_prepr_temp['tot_coll_amt'], 50)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'tot_coll_amt_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1959157564.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr_temp['tot_coll_amt_factor'] = pd.cut(df_inputs_prepr_temp['tot_coll_amt'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1959157564.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_inputs_prepr_temp['tot_coll_amt_factor'] = pd.cut(df_inputs_prepr_temp['tot_coll_amt'], 50)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp.iloc[0 : 50, : ], 90)
#plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '0', '0-110', '110-300', '300-580', '580-1000', '>1000'.
# '> 84' will be the reference category.
df_inputs_prepr['tot_coll_amt:0'] = np.where((df_inputs_prepr['tot_coll_amt'] == 0), 1, 0)
df_inputs_prepr['tot_coll_amt:0-110'] = np.where((df_inputs_prepr['tot_coll_amt'] > 0) & (df_inputs_prepr['tot_coll_amt'] <= 110), 1, 0)
df_inputs_prepr['tot_coll_amt:110-300'] = np.where((df_inputs_prepr['tot_coll_amt'] > 110) & (df_inputs_prepr['tot_coll_amt'] <= 300), 1, 0)
df_inputs_prepr['tot_coll_amt:300-580'] = np.where((df_inputs_prepr['tot_coll_amt'] > 300) & (df_inputs_prepr['tot_coll_amt'] <= 580), 1, 0)
df_inputs_prepr['tot_coll_amt:580-1000'] = np.where((df_inputs_prepr['tot_coll_amt'] > 580) & (df_inputs_prepr['tot_coll_amt'] <= 1000), 1, 0)
df_inputs_prepr['tot_coll_amt:>1000'] = np.where((df_inputs_prepr['tot_coll_amt'] > 1000), 1, 0)

df_inputs_prepr['mort_acc'].unique()

array([ 0.,  2.,  3.,  6.,  5.,  4.,  1.,  7., 10.,  9.,  8., 14., 11.,
       17., 16., 12., 13., 15., 21., 19., 24., 22., 20., 18., 29., 27.,
       34., 28., 31., 23., 35., 25.])

# 'fico_range_high'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'mort_acc', df_targets_prepr)

# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp.iloc[6 : 50, : ], 90)
#plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '0', '1', '2', '3-5', '6-12', '13-18', '>=19'.
# '>=19' will be the reference category
df_inputs_prepr['mort_acc:0'] = np.where(df_inputs_prepr['mort_acc'].isin([0]), 1, 0)
df_inputs_prepr['mort_acc:1'] = np.where(df_inputs_prepr['mort_acc'].isin([1]), 1, 0)
df_inputs_prepr['mort_acc:2'] = np.where(df_inputs_prepr['mort_acc'].isin([2]), 1, 0)
df_inputs_prepr['mort_acc:3-5'] = np.where(df_inputs_prepr['mort_acc'].isin(range(3, 6)), 1, 0)
df_inputs_prepr['mort_acc:6-12'] = np.where(df_inputs_prepr['mort_acc'].isin(range(6, 13)), 1, 0)
df_inputs_prepr['mort_acc:13-18'] = np.where(df_inputs_prepr['mort_acc'].isin(range(13, 19)), 1, 0)
df_inputs_prepr['mort_acc:>=19'] = np.where(df_inputs_prepr['mort_acc'].isin(range(19, 200)), 1, 0)

df_inputs_prepr['months_since_last_credit_pull'].nunique()

128

# 'months_since_last_credit_pull'
# the categories of everyone with 'months_since_last_credit_pull' less or equal 100.
df_inputs_prepr_temp = df_inputs_prepr.loc[(df_inputs_prepr['months_since_last_credit_pull'] <= 1000), : ]

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
# df_inputs_prepr_temp
df_inputs_prepr_temp['months_since_last_credit_pull_factor'] = pd.cut(df_inputs_prepr_temp['months_since_last_credit_pull'], 40)

# Here we do fine-classing: using the 'cut' method, we split the variable into 50 categories by its values.
df_temp = woe_ordered_continuous(df_inputs_prepr_temp, 'months_since_last_credit_pull_factor', df_targets_prepr[df_inputs_prepr_temp.index])
# We calculate weight of evidence.
df_temp

C:\Users\pc\AppData\Local\Temp\ipykernel_6728\790836033.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_inputs_prepr_temp['months_since_last_credit_pull_factor'] = pd.cut(df_inputs_prepr_temp['months_since_last_credit_pull'], 40)
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = pd.concat([df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].count(),
C:\Users\pc\AppData\Local\Temp\ipykernel_6728\1025164655.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df.groupby(df.columns.values[0], as_index = False)[df.columns.values[1]].mean()], axis = 1)

plot_by_woe(df_temp.iloc[ 10: 50, : ], 90)
#plot_by_woe(df_temp, 90)
# We plot the weight of evidence values.

# We create the following categories: '<=30', '30-48', '48-55', '55-110', '>110'.
# '> 110' will be the reference category.
df_inputs_prepr['months_since_last_credit_pull:<=30'] = np.where((df_inputs_prepr['months_since_last_credit_pull'] <= 30), 1, 0)
df_inputs_prepr['months_since_last_credit_pull:30-48'] = np.where((df_inputs_prepr['months_since_last_credit_pull'] > 30) & (df_inputs_prepr['months_since_last_credit_pull'] <= 48), 1, 0)
df_inputs_prepr['months_since_last_credit_pull:48-55'] = np.where((df_inputs_prepr['months_since_last_credit_pull'] > 48) & (df_inputs_prepr['months_since_last_credit_pull'] <= 55), 1, 0)
df_inputs_prepr['months_since_last_credit_pull:55-110'] = np.where((df_inputs_prepr['months_since_last_credit_pull'] > 55) & (df_inputs_prepr['months_since_last_credit_pull'] <= 110), 1, 0)
df_inputs_prepr['months_since_last_credit_pull:>110'] = np.where((df_inputs_prepr['months_since_last_credit_pull'] > 110), 1, 0)

df_inputs_prepr['total_public_records'].unique()

array([ 0.,  8.,  1.,  2., 14.,  4.,  3.,  9., 10.,  6., 93.,  5., 11.,
        7., 12., 16., 18., 13., 19., 21., 49., 20., 25., 37., 17., 15.,
       22., 79., 88., 24., 41., 31., 29., 23., 91.])

# 'fico_range_high'
df_temp = woe_ordered_continuous(df_inputs_prepr, 'total_public_records', df_targets_prepr)

# We calculate weight of evidence.
df_temp

plot_by_woe(df_temp.iloc[: 30, : ], 90)
# We plot the weight of evidence values.

# Categories
# '0', '1-3', '4-12', '>=13'
df_inputs_prepr['total_public_records:0'] = np.where((df_inputs_prepr['total_public_records'] == 0), 1, 0)
df_inputs_prepr['total_public_records:1-3'] = np.where((df_inputs_prepr['total_rev_hi_lim'] >= 1) & (df_inputs_prepr['total_rev_hi_lim'] <= 3), 1, 0)
df_inputs_prepr['total_public_records:4-12'] = np.where((df_inputs_prepr['total_rev_hi_lim'] >= 4) & (df_inputs_prepr['total_rev_hi_lim'] <= 12), 1, 0)
df_inputs_prepr['total_public_records:>=13'] = np.where((df_inputs_prepr['total_public_records'] >= 13), 1, 0)

Final_list_features = ['grade:A', 'grade:B', 'grade:C', 'grade:D', 'grade:E', 'grade:F',
       'grade:G', 'home_ownership:MORTGAGE', 'home_ownership:OWN',
       'home_ownership:RENT_OTHER_NONE_ANY',
       'addr_state:ND_NE_IA_NV_FL_HI_AL', 'addr_state:NM_VA',
       'addr_state:OK_TN_MO_LA_MD_NC', 'addr_state:UT_KY_AZ_NJ',
       'addr_state:AR_MI_PA_OH_MN', 'addr_state:RI_MA_DE_SD_IN',
       'addr_state:GA_WA_OR', 'addr_state:WI_MT', 'addr_state:IL_CT',
       'addr_state:KS_SC_CO_VT_AK_MS', 'addr_state:WV_NH_WY_DC_ME_ID',
       'verification_status:Not Verified',
       'verification_status:Source Verified',
       'verification_status:Verified', 'purpose:debt_consolidation',
       'purpose:credit_card', 'purpose:sm_b__mov__ren_en__house__medic',
       'purpose:other__vacat__maj_purch', 'home_impr__educ__car__wed',
       'initial_list_status:f', 'initial_list_status:w',
       'application_type:Individual', 'application_type:Joint App',
       'hardship_flag:N', 'hardship_flag:Y', 'disbursement_method:Cash',
       'disbursement_method:DirectPay', 'debt_settlement_flag:N',
       'debt_settlement_flag:Y', 'term:36', 'term:60', 'num_tl_120dpd_2m:0', 'num_tl_120dpd_2m:1',
       'num_tl_120dpd_2m:2-6', 'num_tl_30dpd:0', 'num_tl_30dpd:1',
       'num_tl_30dpd:2-4', 'delinq_record_risk_score:0',
       'delinq_record_risk_score:1-2', 'delinq_record_risk_score:3-4',
       'delinq_record_risk_score:5-7', 'log_annual_inc:<20K',
       'annual_inc:20K-30K', 'annual_inc:30K-40K', 'annual_inc:40K-50K',
       'annual_inc:50K-60K', 'annual_inc:60K-70K', 'annual_inc:70K-80K',
       'annual_inc:80K-90K', 'annual_inc:90K-100K',
       'annual_inc:100K-120K', 'annual_inc:120K-140K', 'annual_inc:>140K',
       'loan_amnt:<2500', 'loan_amnt:2500-6500', 'loan_amnt:6500-9500',
       'loan_amnt:9500-11000', 'loan_amnt:11000-17500',
       'loan_amnt:17500-28500', 'loan_amnt:>=28500', 'int_rate:<=8',
       'int_rate:8-12.5', 'int_rate:12.5-16.5', 'int_rate:16.5-20',
       'int_rate:20-23.5', 'int_rate:>23.5', 'emp_length_int:0',
       'emp_length_int:1', 'emp_length_int:2-4', 'emp_length_int:5-7',
       'emp_length_int:8-9', 'emp_length_int:10', 'dti:<=10', 'dti:10-20',
       'dti:20-30', 'dti:30-40', 'dti:>40', 'min_mths_since_delinquency',
       'min_mths_since_delinquency:Missing',
       'min_mths_since_delinquency:<=20',
       'min_mths_since_delinquency:20-40',
       'min_mths_since_delinquency:40-80',
       'min_mths_since_delinquency:>80',
       'mths_since_earliest_cr_line:<=120',
       'mths_since_earliest_cr_line:121-200',
       'mths_since_earliest_cr_line:201-260',
       'mths_since_earliest_cr_line:261-320',
       'mths_since_earliest_cr_line:321-400',
       'mths_since_earliest_cr_line:401-600',
       'mths_since_earliest_cr_line:>=601', 'delinq_2yrs:0',
       'delinq_2yrs:1', 'delinq_2yrs:2-9', 'delinq_2yrs:>=10',
       'inq_last_6mths:0', 'inq_last_6mths:1-2', 'inq_last_6mths:3-5',
       'inq_last_6mths:>=6', 'collections_12_mths_ex_med:0',
       'collections_12_mths_ex_med:1', 'collections_12_mths_ex_med:>=2',
       'chargeoff_within_12_mths:0', 'chargeoff_within_12_mths:1',
       'chargeoff_within_12_mths:>=2', 'total_acc:<=20',
       'total_acc:21-56', 'total_acc:>=57', 'delinq_amnt:0',
       'delinq_amnt:>=1', 'num_accts_ever_120_pd:0',
       'num_accts_ever_120_pd:1-11', 'num_accts_ever_120_pd:>=12',
       'num_tl_90g_dpd_24m:0', 'num_tl_90g_dpd_24m:1-4',
       'num_tl_90g_dpd_24m:>=5', 'revol_bal:<=8k', 'revol_bal:8-22k',
       'revol_bal:22-35k', 'revol_bal:35-60k', 'revol_bal:60-100k',
       'revol_bal:>100k', 'total_bal_il:=0', 'total_bal_il:0-18k',
       'total_bal_il:18-30k', 'total_bal_il:30-70k',
       'total_bal_il:70-200k', 'total_bal_il:>200k', 'max_bal_bc:=0',
       'max_bal_bc:0-8k', 'max_bal_bc:8-16k', 'max_bal_bc:16-26k',
       'max_bal_bc:26-50k', 'max_bal_bc:>50k', 'avg_cur_bal:0-7k',
       'avg_cur_bal:7-15k', 'avg_cur_bal:15-30k', 'avg_cur_bal:30-50k',
       'avg_cur_bal:50-100k', 'avg_cur_bal:>100k', 'bc_open_to_buy:0-5k',
       'bc_open_to_buy:5-15k', 'bc_open_to_buy:15-30k',
       'bc_open_to_buy:30-50k', 'bc_open_to_buy:50-100k',
       'bc_open_to_buy:>100k', 'revol_bal_to_bc_limit:0-0.6',
       'revol_bal_to_bc_limit:0.6-1.2', 'revol_bal_to_bc_limit:1.2-3.6',
       'revol_bal_to_bc_limit:3.6-5.5', 'revol_bal_to_bc_limit:5.5-10.',
       'revol_bal_to_bc_limit:>10.', 'revol_bal_to_open_to_buy:0-2',
       'revol_bal_to_open_to_buy:2-4', 'revol_bal_to_open_to_buy:4-20',
       'revol_bal_to_open_to_buy:20-100', 'revol_bal_to_open_to_buy:>100',
       'total_bal_ex_mort_to_inc:0-0.4', 'total_bal_ex_mort_to_inc:0.4-1',
       'total_bal_ex_mort_to_inc:1-2.6', 'total_bal_ex_mort_to_inc:2.6-4.4',
       'total_bal_ex_mort_to_inc:>4.4', 'total_balance_to_credit_ratio:0-0.05',
       'total_balance_to_credit_ratio:0.05-0.2', 'total_balance_to_credit_ratio:0.2-0.4',
       'total_balance_to_credit_ratio:0.4-0.7', 'total_balance_to_credit_ratio:0.7-1',
       'total_balance_to_credit_ratio:1-1.4', 'total_balance_to_credit_ratio:>1.4',
       'rev_to_il_limit_ratio:0-0.6', 'rev_to_il_limit_ratio:0.6-0.8',
       'rev_to_il_limit_ratio:0.8-1.8', 'rev_to_il_limit_ratio:1.8-4.5',
       'rev_to_il_limit_ratio:4.5-10', 'rev_to_il_limit_ratio:>10.',
       'total_il_high_credit_limit:0-5k', 'total_il_high_credit_limit:5-10k',
       'total_il_high_credit_limit:10-30k', 'total_il_high_credit_limit:30-35k',
       'total_il_high_credit_limit:35-100k', 'total_il_high_credit_limit:>100k', 
       'tot_cur_bal:0-20k', 'tot_cur_bal:20-70k', 'tot_cur_bal:70-80k', 
       'tot_cur_bal:80-130k', 'tot_cur_bal:130-200k', 'tot_cur_bal:200-250k',
       'tot_cur_bal:250-500k', 'tot_cur_bal:>500k', 'open_act_il:0',
       'open_act_il:1-5', 'open_act_il:6-15', 'open_act_il:>=16',
       'open_il_12m:0', 'open_il_12m:1-5', 'open_il_12m:>=6',
       'num_actv_rev_tl:0', 'num_actv_rev_tl:1-5', 'num_actv_rev_tl:6-9',
       'num_actv_rev_tl:10-13', 'num_actv_rev_tl:14-17',
       'num_actv_rev_tl:18-26', 'num_actv_rev_tl:>=27', 'open_rv_12m:0',
       'open_rv_12m:1-2', 'open_rv_12m:3-5', 'open_rv_12m:6-8',
       'open_rv_12m:9-13', 'open_rv_12m:>=14', 'num_bc_tl:0',
       'num_bc_tl:1-5', 'num_bc_tl:6-10', 'num_bc_tl:11-20',
       'num_bc_tl:21-32', 'num_bc_tl:>=33', 'open_acc_6m:0',
       'open_acc_6m:1-3', 'open_acc_6m:4-7', 'open_acc_6m:>=8',
       'acc_open_past_24mths:0-3', 'acc_open_past_24mths:4-7',
       'acc_open_past_24mths:8-13', 'acc_open_past_24mths:14-21',
       'acc_open_past_24mths:>=22', 'total_cu_tl:0', 'total_cu_tl:1-7',
       'total_cu_tl:8-17', 'total_cu_tl:>=18', 'inq_last_12m:0',
       'inq_last_12m:1-4', 'inq_last_12m:5-9', 'inq_last_12m:10-16',
       'inq_last_12m:>=17', 'mths_since_recent_inq:Missing',
       'mths_since_recent_inq:0-1', 'mths_since_recent_inq:2-3',
       'mths_since_recent_inq:4-6', 'mths_since_recent_inq:7-10',
       'mths_since_recent_inq:11-15', 'mths_since_recent_inq:>=16',
       'out_prncp:=0', 'out_prncp:>0', 'last_pymnt_amnt:<=200',
       'last_pymnt_amnt:200-700', 'last_pymnt_amnt:700-1000',
       'last_pymnt_amnt:1000-1500', 'last_pymnt_amnt:1500-2600',
       'last_pymnt_amnt:2600-10000', 'last_pymnt_amnt:>10000',
       'principal_paid_ratio:<=0.3', 'principal_paid_ratio:0.3-0.45',
       'principal_paid_ratio:0.45-0.6', 'principal_paid_ratio:0.6-1',
       'principal_paid_ratio:=1', 'fico_range_high:<=680',
       'fico_range_high:680-700', 'fico_range_high:700-720',
       'fico_range_high:720-750', 'fico_range_high:750-795',
       'fico_range_high:>795', 'last_fico_range_high:<=520',
       'last_fico_range_high:520-550', 'last_fico_range_high:550-580',
       'last_fico_range_high:580-610', 'last_fico_range_high:610-640',
       'last_fico_range_high:640-670', 'last_fico_range_high:>670',
       'mo_sin_rcnt_rev_tl_op:0-3', 'mo_sin_rcnt_rev_tl_op:3-6',
       'mo_sin_rcnt_rev_tl_op:6-9', 'mo_sin_rcnt_rev_tl_op:9-20',
       'mo_sin_rcnt_rev_tl_op:20-37', 'mo_sin_rcnt_rev_tl_op:37-63',
       'mo_sin_rcnt_rev_tl_op:63-80', 'mo_sin_rcnt_rev_tl_op:80-140',
       'mo_sin_rcnt_rev_tl_op:>140', 'mo_sin_rcnt_tl:0-2',
       'mo_sin_rcnt_tl:2-5', 'mo_sin_rcnt_tl:5-6', 'mo_sin_rcnt_tl:6-10',
       'mo_sin_rcnt_tl:10-15', 'mo_sin_rcnt_tl:15-20',
       'mo_sin_rcnt_tl:20-50', 'mo_sin_rcnt_tl:>50',
       'mths_since_rcnt_il:0-4', 'mths_since_rcnt_il:4-10',
       'mths_since_rcnt_il:10-20', 'mths_since_rcnt_il:20-40',
       'mths_since_rcnt_il:40-100', 'mths_since_rcnt_il:>100',
       'mths_since_recent_bc:0-12', 'mths_since_recent_bc:12-32',
       'mths_since_recent_bc:32-52', 'mths_since_recent_bc:52-68',
       'mths_since_recent_bc:68-100', 'mths_since_recent_bc:100-130',
       'mths_since_recent_bc:>130', 'mths_since_rcnt_il:Missing',
       'mths_since_recent_revol_delinq:0-20', 'mths_since_recent_revol_delinq:20-34',
       'mths_since_recent_revol_delinq:34-50', 'mths_since_recent_revol_delinq:50-84',
       'mths_since_recent_revol_delinq:>84', 'mths_since_recent_revol_delinq:Missing', 
       'percent_bc_gt_75:0-4', 'percent_bc_gt_75:4-20', 'percent_bc_gt_75:20-40',
       'percent_bc_gt_75:40-70', 'percent_bc_gt_75:70-96',
       'percent_bc_gt_75:>96', 'pub_rec_bankruptcies:0',
       'pub_rec_bankruptcies:1-3', 'pub_rec_bankruptcies:>4',
       'tot_coll_amt:0', 'tot_coll_amt:0-110', 'tot_coll_amt:110-300',
       'tot_coll_amt:300-580', 'tot_coll_amt:580-1000',
       'tot_coll_amt:>1000', 'mort_acc:0', 'mort_acc:1', 'mort_acc:2',
       'mort_acc:3-5', 'mort_acc:6-12', 'mort_acc:13-18', 'mort_acc:>=19',
       'months_since_last_credit_pull:<=30', 'months_since_last_credit_pull:30-48',
       'months_since_last_credit_pull:48-55', 'months_since_last_credit_pull:55-110',
       'months_since_last_credit_pull:>110', 'total_public_records:0',
       'total_public_records:1-3', 'total_public_records:4-12',
       'total_public_records:>=13']

len(Final_list_features)

344

features_to_drop = ['loan_amnt', 'int_rate', 'grade', 'home_ownership', 'annual_inc',
       'verification_status', 'purpose', 'addr_state', 'dti',
       'delinq_2yrs', 'fico_range_high', 'inq_last_6mths', 'revol_bal',
       'total_acc', 'initial_list_status', 'out_prncp', 'last_pymnt_amnt',
       'last_fico_range_high', 'collections_12_mths_ex_med',
       'application_type', 'tot_coll_amt', 'tot_cur_bal', 'open_acc_6m',
       'open_act_il', 'open_il_12m', 'mths_since_rcnt_il', 'total_bal_il',
       'open_rv_12m', 'max_bal_bc', 'total_rev_hi_lim', 'inq_fi',
       'total_cu_tl', 'inq_last_12m', 'acc_open_past_24mths',
       'avg_cur_bal', 'bc_open_to_buy', 'chargeoff_within_12_mths',
       'delinq_amnt', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl',
       'mort_acc', 'mths_since_recent_bc', 'mths_since_recent_inq',
       'mths_since_recent_revol_delinq', 'num_accts_ever_120_pd',
       'num_actv_rev_tl', 'num_bc_tl', 'num_tl_120dpd_2m', 'num_tl_30dpd',
       'num_tl_90g_dpd_24m', 'num_tl_op_past_12m', 'percent_bc_gt_75',
       'pub_rec_bankruptcies', 'hardship_flag', 'disbursement_method',
       'debt_settlement_flag', 'emp_length_int', 'term_int',
       'mths_since_earliest_cr_line', 'months_since_last_credit_pull', 
       'delinq_record_risk_score', 'revol_bal_to_bc_limit', 'revol_bal_to_open_to_buy',
       'total_bal_ex_mort_to_inc', 'total_balance_to_credit_ratio', 'rev_to_il_limit_ratio', 
       'principal_paid_ratio', 'total_public_records', 'total_il_high_credit_limit']

len(features_to_drop)

69

# Drop the original features.
df_inputs_prepr_copy = df_inputs_prepr.copy()
df_inputs_prepr = df_inputs_prepr.drop(columns = features_to_drop)

df_inputs_prepr.shape[1]

344

#####
# loan_data_inputs_train = df_inputs_prepr.copy()
#####
loan_data_inputs_test = df_inputs_prepr.copy()

# save and export datasets in csv format.
loan_data_inputs_train.to_csv('C:/Disc D/365DataScience/Credit risk modeling/loan_data_inputs_train.csv')
loan_data_targets_train.to_csv('C:/Disc D/365DataScience/Credit risk modeling/loan_data_targets_train.csv')
loan_data_inputs_test.to_csv('C:/Disc D/365DataScience/Credit risk modeling/loan_data_inputs_test.csv')
loan_data_targets_test.to_csv('C:/Disc D/365DataScience/Credit risk modeling/loan_data_targets_test.csv')

# shape of train dataset.
loan_data_inputs_train.shape

(1096932, 344)

# shape of test dataset.
loan_data_inputs_test.shape

(274234, 344)

	Amount Requested	Application Date	Loan Title	Risk_Score	Debt-To-Income Ratio	Zip Code	State	Employment Length
8910244	15000.0	2014-08-21	debt_consolidation	603.0	32.33%	471xx	IN	< 1 year
12365756	25000.0	2017-08-06	Credit card refinancing	622.0	46.58%	713xx	LA	< 1 year
18011080	25000.0	2018-05-01	Debt consolidation	NaN	54.75%	322xx	FL	< 1 year
11160554	15000.0	2018-03-13	Debt consolidation	NaN	11.5%	112xx	NY	< 1 year
17248026	2500.0	2018-12-31	Car financing	NaN	0.54%	453xx	OH	1 year

	grade	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	A	47247	0.068428	0.172287	3233.0	44014.0	0.054981	0.204306	-1.312628	NaN	NaN	0.437964
1	B	79943	0.146042	0.291514	11675.0	68268.0	0.198548	0.316889	-0.467522	0.077614	0.845106	0.437964
2	C	77916	0.241825	0.284122	18842.0	59074.0	0.320431	0.274212	0.155767	0.095783	0.623289	0.437964
3	D	41485	0.321827	0.151276	13351.0	28134.0	0.227050	0.130593	0.553082	0.080003	0.397315	0.437964
4	E	19227	0.402403	0.070112	7737.0	11490.0	0.131577	0.053335	0.903006	0.080576	0.349924	0.437964
5	F	6599	0.463252	0.024063	3057.0	3542.0	0.051988	0.016441	1.151212	0.060849	0.248206	0.437964
6	G	1817	0.499174	0.006626	907.0	910.0	0.015425	0.004224	1.295167	0.035922	0.143955	0.437964

	home_ownership	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	NONE	12	0.166667	0.000044	2.0	10.0	0.000034	0.000046	-0.310968	NaN	NaN	0.031033
1	MORTGAGE	135489	0.185329	0.494063	25110.0	110379.0	0.427026	0.512361	-0.182184	0.018662	0.128784	0.031033
2	OWN	29698	0.223012	0.108294	6623.0	23075.0	0.112632	0.107110	0.050268	0.037683	0.232452	0.031033
3	RENT	108950	0.248233	0.397288	27045.0	81905.0	0.459933	0.380190	0.190412	0.025221	0.140143	0.031033
4	OTHER	36	0.250000	0.000131	9.0	27.0	0.000153	0.000125	0.199857	0.001767	0.009446	0.031033
5	ANY	49	0.265306	0.000179	13.0	36.0	0.000221	0.000167	0.279900	0.015306	0.080043	0.031033

	addr_state	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	IA	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	-inf	NaN	NaN	inf
1	ME	395	0.121519	0.001440	48.0	347.0	0.000816	0.001611	-0.679654	0.121519	inf	inf
2	NH	1293	0.146945	0.004715	190.0	1103.0	0.003231	0.005120	-0.460296	0.025426	0.219359	inf
3	DC	696	0.152299	0.002538	106.0	590.0	0.001803	0.002739	-0.418214	0.005354	0.042082	inf
4	OR	3271	0.153470	0.011928	502.0	2769.0	0.008537	0.012853	-0.409172	0.001171	0.009042	inf
5	VT	517	0.160542	0.001885	83.0	434.0	0.001412	0.002015	-0.355734	0.007072	0.053437	inf
6	KS	2261	0.163644	0.008245	370.0	1891.0	0.006292	0.008778	-0.332889	0.003103	0.022846	inf
7	CO	6119	0.165550	0.022313	1013.0	5106.0	0.017227	0.023701	-0.319031	0.001906	0.013858	inf
8	WY	601	0.166389	0.002192	100.0	501.0	0.001701	0.002326	-0.312966	0.000839	0.006064	inf
9	WV	1015	0.166502	0.003701	169.0	846.0	0.002874	0.003927	-0.312151	0.000113	0.000815	inf
10	RI	1226	0.177814	0.004471	218.0	1008.0	0.003707	0.004679	-0.232759	0.011312	0.079392	inf
11	WA	5989	0.182167	0.021839	1091.0	4898.0	0.018554	0.022736	-0.203263	0.004353	0.029496	inf
12	ND	318	0.182390	0.001160	58.0	260.0	0.000986	0.001207	-0.201769	0.000223	0.001494	inf
13	SC	3262	0.182403	0.011895	595.0	2667.0	0.010119	0.012380	-0.201679	0.000013	0.000091	inf
14	UT	2064	0.185078	0.007526	382.0	1682.0	0.006496	0.007808	-0.183849	0.002674	0.017830	inf
15	CT	4031	0.188787	0.014699	761.0	3270.0	0.012942	0.015179	-0.159442	0.003709	0.024406	inf
16	IL	10477	0.189367	0.038205	1984.0	8493.0	0.033740	0.039423	-0.155658	0.000580	0.003785	inf
17	WI	3544	0.189898	0.012923	673.0	2871.0	0.011445	0.013327	-0.152201	0.000531	0.003457	inf
18	MA	6381	0.200282	0.023268	1278.0	5103.0	0.021734	0.023687	-0.086063	0.010384	0.066138	inf
19	HI	1360	0.201471	0.004959	274.0	1086.0	0.004660	0.005041	-0.078659	0.001189	0.007404	inf
20	GA	8846	0.203595	0.032257	1801.0	7045.0	0.030628	0.032702	-0.065507	0.002124	0.013152	inf
21	DE	765	0.205229	0.002790	157.0	608.0	0.002670	0.002822	-0.055460	0.001634	0.010047	inf
22	MN	4862	0.205265	0.017729	998.0	3864.0	0.016972	0.017936	-0.055235	0.000037	0.000224	inf
23	MT	761	0.210250	0.002775	160.0	601.0	0.002721	0.002790	-0.024952	0.004984	0.030284	inf
24	SD	585	0.211966	0.002133	124.0	461.0	0.002109	0.002140	-0.014647	0.001716	0.010305	inf
25	CA	40010	0.213472	0.145897	8541.0	31469.0	0.145250	0.146074	-0.005655	0.001506	0.008992	inf
26	NC	7750	0.215097	0.028261	1667.0	6083.0	0.028349	0.028236	0.003997	0.001625	0.009652	inf
27	TX	22218	0.215276	0.081018	4783.0	17435.0	0.081341	0.080930	0.005058	0.000179	0.001061	inf
28	AZ	6583	0.215707	0.024005	1420.0	5163.0	0.024149	0.023966	0.007609	0.000431	0.002551	inf
29	AK	649	0.215716	0.002367	140.0	509.0	0.002381	0.002363	0.007664	0.000009	0.000055	inf
30	KY	2642	0.216124	0.009634	571.0	2071.0	0.009711	0.009613	0.010072	0.000408	0.002408	inf
31	IN	4508	0.218057	0.016439	983.0	3525.0	0.016717	0.016362	0.021443	0.001933	0.011371	inf
32	MO	4352	0.218061	0.015870	949.0	3403.0	0.016139	0.015796	0.021466	0.000004	0.000023	inf
33	MI	7295	0.218780	0.026601	1596.0	5699.0	0.027142	0.026454	0.025679	0.000719	0.004214	inf
34	OH	8954	0.220013	0.032651	1970.0	6984.0	0.033502	0.032419	0.032881	0.001233	0.007202	inf
35	PA	9237	0.220526	0.033683	2037.0	7200.0	0.034642	0.033421	0.035867	0.000513	0.002985	inf
36	VA	7728	0.223732	0.028180	1729.0	5999.0	0.029404	0.027846	0.054420	0.003206	0.018553	inf
37	TN	4096	0.225342	0.014936	923.0	3173.0	0.015697	0.014729	0.063666	0.001610	0.009246	inf
38	FL	19831	0.227220	0.072314	4506.0	15325.0	0.076630	0.071136	0.074394	0.001878	0.010728	inf
39	NJ	9932	0.227547	0.036217	2260.0	7672.0	0.038434	0.035612	0.076257	0.000327	0.001863	inf
40	NM	1464	0.228142	0.005339	334.0	1130.0	0.005680	0.005245	0.079638	0.000595	0.003381	inf
41	NV	4091	0.230017	0.014918	941.0	3150.0	0.016003	0.014622	0.090255	0.001875	0.010617	inf
42	MD	6357	0.236118	0.023181	1501.0	4856.0	0.025526	0.022541	0.124386	0.006101	0.034131	inf
43	NY	22427	0.238329	0.081781	5345.0	17082.0	0.090898	0.079292	0.136606	0.002211	0.012220	inf
44	AL	3323	0.242552	0.012117	806.0	2517.0	0.013707	0.011684	0.159730	0.004223	0.023124	inf
45	LA	3183	0.251021	0.011607	799.0	2384.0	0.013588	0.011066	0.205295	0.008469	0.045565	inf
46	ID	307	0.254072	0.001119	78.0	229.0	0.001326	0.001063	0.221456	0.003051	0.016161	inf
47	OK	2590	0.264479	0.009444	685.0	1905.0	0.011649	0.008843	0.275651	0.010407	0.054195	inf
48	AR	2022	0.266568	0.007373	539.0	1483.0	0.009166	0.006884	0.286363	0.002089	0.010712	inf
49	NE	741	0.271255	0.002702	201.0	540.0	0.003418	0.002507	0.310205	0.004687	0.023843	inf
50	MS	1304	0.278374	0.004755	363.0	941.0	0.006173	0.004368	0.345929	0.007119	0.035724	inf

	verification_status	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	Not Verified	82536	0.160887	0.300969	13279.0	69257.0	0.225826	0.321480	-0.353171	NaN	NaN	0.050456
1	Source Verified	106486	0.225344	0.388303	23996.0	82490.0	0.408081	0.382905	0.063680	0.064457	0.416850	0.050456
2	Verified	85212	0.252629	0.310727	21527.0	63685.0	0.366093	0.295615	0.213828	0.027285	0.150149	0.050456

	id	member_id	loan_amnt	funded_amnt	funded_amnt_inv	term	int_rate	installment	grade	sub_grade	emp_title	emp_length	home_ownership	annual_inc	verification_status	issue_d	loan_status	pymnt_plan	url	desc	purpose	title	zip_code	addr_state	dti	delinq_2yrs	earliest_cr_line	fico_range_low	fico_range_high	inq_last_6mths	mths_since_last_delinq	mths_since_last_record	open_acc	revol_bal	revol_util	total_acc	initial_list_status	out_prncp	out_prncp_inv	total_pymnt	total_pymnt_inv	total_rec_prncp	total_rec_int	last_pymnt_d	last_pymnt_amnt	next_pymnt_d	last_credit_pull_d	last_fico_range_high	last_fico_range_low	mths_since_last_major_derog	policy_code	application_type	annual_inc_joint	dti_joint	verification_status_joint	tot_coll_amt	tot_cur_bal	open_acc_6m	open_act_il	open_il_24m	mths_since_rcnt_il	total_bal_il	il_util	open_rv_12m	open_rv_24m	max_bal_bc	all_util	total_rev_hi_lim	inq_fi	total_cu_tl	inq_last_12m	acc_open_past_24mths	avg_cur_bal	bc_open_to_buy	bc_util	mo_sin_old_il_acct	mo_sin_old_rev_tl_op	mo_sin_rcnt_rev_tl_op	mo_sin_rcnt_tl	mort_acc	mths_since_recent_bc	mths_since_recent_bc_dlq	mths_since_recent_inq	mths_since_recent_revol_delinq	num_accts_ever_120_pd	num_actv_bc_tl	num_actv_rev_tl	num_bc_sats	num_bc_tl	num_il_tl	num_op_rev_tl	num_rev_accts	num_rev_tl_bal_gt_0	num_sats	num_tl_op_past_12m	pct_tl_nvr_dlq	percent_bc_gt_75	tot_hi_cred_lim	total_bal_ex_mort	total_bc_limit	total_il_high_credit_limit	revol_bal_joint	sec_app_fico_range_low	sec_app_fico_range_high	sec_app_earliest_cr_line	sec_app_inq_last_6mths	sec_app_mort_acc	sec_app_open_acc	sec_app_revol_util	sec_app_open_act_il	sec_app_num_rev_accts	sec_app_chargeoff_within_12_mths	sec_app_collections_12_mths_ex_med	sec_app_mths_since_last_major_derog	hardship_flag	hardship_type	hardship_reason	hardship_status	deferral_term	hardship_amount	hardship_start_date	hardship_end_date	payment_plan_start_date	hardship_length	hardship_dpd	hardship_loan_status	orig_projected_additional_accrued_interest	hardship_payoff_balance_amount	hardship_last_payment_amount	disbursement_method	debt_settlement_flag	debt_settlement_flag_date	settlement_status	settlement_date	settlement_amount	settlement_percentage	settlement_term
0	68407277	NaN	3600.0	3600.0	3600.0	36 months	13.99	123.03	C	C4	leadman	10+ years	MORTGAGE	55000.0	Not Verified	Dec-2015	Fully Paid	n	https://lendingclub.com/browse/loanDetail.acti...	NaN	debt_consolidation	Debt consolidation	190xx	PA	5.91	0.0	Aug-2003	675.0	679.0	1.0	30.0	NaN	7.0	2765.0	29.7	13.0	w	0.00	0.00	4421.723917	4421.72	3600.00	821.72	Jan-2019	122.67	NaN	Mar-2019	564.0	560.0	30.0	1.0	Individual	NaN	NaN	NaN	722.0	144904.0	2.0	2.0	1.0	21.0	4981.0	36.0	3.0	3.0	722.0	34.0	9300.0	3.0	1.0	4.0	4.0	20701.0	1506.0	37.2	148.0	128.0	3.0	3.0	1.0	4.0	69.0	4.0	69.0	2.0	2.0	4.0	2.0	5.0	3.0	4.0	9.0	4.0	7.0	3.0	76.9	0.0	178050.0	7746.0	2400.0	13734.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	N	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Cash	N	NaN	NaN	NaN	NaN	NaN	NaN
1	68355089	NaN	24700.0	24700.0	24700.0	36 months	11.99	820.28	C	C1	Engineer	10+ years	MORTGAGE	65000.0	Not Verified	Dec-2015	Fully Paid	n	https://lendingclub.com/browse/loanDetail.acti...	NaN	small_business	Business	577xx	SD	16.06	1.0	Dec-1999	715.0	719.0	4.0	6.0	NaN	22.0	21470.0	19.2	38.0	w	0.00	0.00	25679.660000	25679.66	24700.00	979.66	Jun-2016	926.35	NaN	Mar-2019	699.0	695.0	NaN	1.0	Individual	NaN	NaN	NaN	0.0	204396.0	1.0	1.0	1.0	19.0	18005.0	73.0	2.0	3.0	6472.0	29.0	111800.0	0.0	0.0	6.0	4.0	9733.0	57830.0	27.1	113.0	192.0	2.0	2.0	4.0	2.0	NaN	0.0	6.0	0.0	5.0	5.0	13.0	17.0	6.0	20.0	27.0	5.0	22.0	2.0	97.4	7.7	314017.0	39475.0	79300.0	24667.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	N	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Cash	N	NaN	NaN	NaN	NaN	NaN	NaN
2	68341763	NaN	20000.0	20000.0	20000.0	60 months	10.78	432.66	B	B4	truck driver	10+ years	MORTGAGE	63000.0	Not Verified	Dec-2015	Fully Paid	n	https://lendingclub.com/browse/loanDetail.acti...	NaN	home_improvement	NaN	605xx	IL	10.78	0.0	Aug-2000	695.0	699.0	0.0	NaN	NaN	6.0	7869.0	56.2	18.0	w	0.00	0.00	22705.924294	22705.92	20000.00	2705.92	Jun-2017	15813.30	NaN	Mar-2019	704.0	700.0	NaN	1.0	Joint App	71000.0	13.85	Not Verified	0.0	189699.0	0.0	1.0	4.0	19.0	10827.0	73.0	0.0	2.0	2081.0	65.0	14000.0	2.0	5.0	1.0	6.0	31617.0	2737.0	55.9	125.0	184.0	14.0	14.0	5.0	101.0	NaN	10.0	NaN	0.0	2.0	3.0	2.0	4.0	6.0	4.0	7.0	3.0	6.0	0.0	100.0	50.0	218418.0	18696.0	6200.0	14877.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	N	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Cash	N	NaN	NaN	NaN	NaN	NaN	NaN
3	66310712	NaN	35000.0	35000.0	35000.0	60 months	14.85	829.90	C	C5	Information Systems Officer	10+ years	MORTGAGE	110000.0	Source Verified	Dec-2015	Current	n	https://lendingclub.com/browse/loanDetail.acti...	NaN	debt_consolidation	Debt consolidation	076xx	NJ	17.06	0.0	Sep-2008	785.0	789.0	0.0	NaN	NaN	13.0	7802.0	11.6	17.0	w	15897.65	15897.65	31464.010000	31464.01	19102.35	12361.66	Feb-2019	829.90	Apr-2019	Mar-2019	679.0	675.0	NaN	1.0	Individual	NaN	NaN	NaN	0.0	301500.0	1.0	1.0	1.0	23.0	12609.0	70.0	1.0	1.0	6987.0	45.0	67300.0	0.0	1.0	0.0	2.0	23192.0	54962.0	12.1	36.0	87.0	2.0	2.0	1.0	2.0	NaN	NaN	NaN	0.0	4.0	5.0	8.0	10.0	2.0	10.0	13.0	5.0	13.0	1.0	100.0	0.0	381215.0	52226.0	62500.0	18000.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	N	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Cash	N	NaN	NaN	NaN	NaN	NaN	NaN
4	68476807	NaN	10400.0	10400.0	10400.0	60 months	22.45	289.91	F	F1	Contract Specialist	3 years	MORTGAGE	104433.0	Source Verified	Dec-2015	Fully Paid	n	https://lendingclub.com/browse/loanDetail.acti...	NaN	major_purchase	Major purchase	174xx	PA	25.37	1.0	Jun-1998	695.0	699.0	3.0	12.0	NaN	12.0	21929.0	64.5	35.0	w	0.00	0.00	11740.500000	11740.50	10400.00	1340.50	Jul-2016	10128.96	NaN	Mar-2018	704.0	700.0	NaN	1.0	Individual	NaN	NaN	NaN	0.0	331730.0	1.0	3.0	3.0	14.0	73839.0	84.0	4.0	7.0	9702.0	78.0	34000.0	2.0	1.0	3.0	10.0	27644.0	4567.0	77.5	128.0	210.0	4.0	4.0	6.0	4.0	12.0	1.0	12.0	0.0	4.0	6.0	5.0	9.0	10.0	7.0	19.0	6.0	12.0	4.0	96.6	60.0	439570.0	95768.0	20300.0	88097.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	N	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Cash	N	NaN	NaN	NaN	NaN	NaN	NaN

	id	loan_amnt	funded_amnt	funded_amnt_inv	term	int_rate	installment	grade	sub_grade	home_ownership	annual_inc	verification_status	issue_d	loan_status	pymnt_plan	purpose	title	zip_code	addr_state	dti	delinq_2yrs	earliest_cr_line	fico_range_low	fico_range_high	inq_last_6mths	mths_since_last_delinq	mths_since_last_record	open_acc	revol_bal	revol_util	total_acc	initial_list_status	out_prncp	out_prncp_inv	total_pymnt	total_pymnt_inv	total_rec_prncp	total_rec_int	last_pymnt_d	last_pymnt_amnt	next_pymnt_d	last_credit_pull_d	last_fico_range_high	last_fico_range_low	mths_since_last_major_derog	policy_code	application_type	acc_now_delinq	tot_coll_amt	tot_cur_bal	open_acc_6m	open_act_il	open_il_24m	mths_since_rcnt_il	total_bal_il	il_util	open_rv_12m	open_rv_24m	max_bal_bc	all_util	total_rev_hi_lim	inq_fi	inq_last_12m	acc_open_past_24mths	avg_cur_bal	bc_open_to_buy	bc_util	mo_sin_old_il_acct	mo_sin_old_rev_tl_op	mo_sin_rcnt_rev_tl_op	mo_sin_rcnt_tl	mort_acc	mths_since_recent_bc	mths_since_recent_bc_dlq	mths_since_recent_inq	mths_since_recent_revol_delinq	num_accts_ever_120_pd	num_actv_bc_tl	num_actv_rev_tl	num_bc_sats	num_bc_tl	num_il_tl	num_op_rev_tl	num_rev_accts	num_rev_tl_bal_gt_0	num_sats	num_tl_120dpd_2m	num_tl_90g_dpd_24m	num_tl_op_past_12m	pct_tl_nvr_dlq	percent_bc_gt_75	tot_hi_cred_lim	total_bal_ex_mort	total_bc_limit	total_il_high_credit_limit	hardship_flag	disbursement_method	debt_settlement_flag	emp_length_int	term_int	issue_d_date	mths_since_issue_d	target	earliest_cr_line_date	mths_since_earliest_cr_line	months_since_last_pymnt	months_since_last_credit_pull
963330	106165131	18000.0	18000.0	18000.0	60 months	22.74	504.75	E	E1	RENT	108000.0	Verified	Apr-2017	Current	n	debt_consolidation	Debt consolidation	936xx	CA	15.31	1.0	Aug-1994	660.0	664.0	1.0	2.0	999.0	18.0	12074.0	37.4	37.0	w	13337.99	13337.99	11586.510000	11586.51	4662.01	6924.50	2019-03-01	504.75	Apr-2019	2019-03-01	599.0	595.0	2.0	1.0	Individual	1.0	0.0	37135.0	1.0	2.0	1.0	21.0	25061.0	75.0	2.0	4.0	1250.0	57.0	32300.0	0.0	3.0	5.0	2184.0	4042.0	39.7	179.0	272.0	5.0	5.0	1.0	41.0	999.0	3.0	999.0	5.0	3.0	11.0	4.0	8.0	12.0	16.0	24.0	11.0	18.0	1.0	1.0	2.0	83.8	25.0	65700.0	37135.0	6700.0	33400.0	N	Cash	N	10.0	60.0	2017-04-01	45.0	0	1994-08-01	321.0	22.0	22.0
260707	51006181	20000.0	20000.0	20000.0	60 months	15.61	482.23	D	D1	MORTGAGE	92500.0	Verified	Jun-2015	Current	n	debt_consolidation	Debt consolidation	920xx	CA	13.40	2.0	Jul-1999	665.0	669.0	2.0	15.0	999.0	9.0	24710.0	90.5	25.0	f	6532.82	6532.82	21683.010000	21683.01	13467.18	8215.83	2019-03-01	482.23	Apr-2019	2019-03-01	699.0	695.0	15.0	1.0	Individual	0.0	0.0	279997.0	0.0	0.0	0.0	999.0	0.0	72.0	0.0	0.0	0.0	58.0	27300.0	0.0	0.0	4.0	31111.0	646.0	97.1	183.0	190.0	4.0	4.0	1.0	4.0	27.0	4.0	27.0	3.0	5.0	7.0	5.0	13.0	8.0	7.0	16.0	7.0	9.0	0.0	2.0	4.0	79.2	100.0	333252.0	25337.0	22300.0	852.0	N	Cash	N	10.0	60.0	2015-06-01	67.0	0	1999-07-01	261.0	22.0	22.0
1895814	1825127	5600.0	5600.0	5575.0	36 months	12.12	186.33	B	B3	RENT	48000.0	Verified	Nov-2012	Fully Paid	n	credit_card	Card Refinance	900xx	CA	22.09	0.0	Mar-2004	680.0	684.0	0.0	999.0	999.0	9.0	16158.0	62.1	21.0	f	0.00	0.00	6683.299998	6653.46	5600.00	1083.30	2015-06-01	1097.89	No Payment Due	2017-07-01	509.0	505.0	999.0	1.0	Individual	0.0	0.0	22808.0	0.0	0.0	0.0	999.0	0.0	72.0	0.0	0.0	0.0	58.0	26000.0	0.0	0.0	1.0	2534.0	512.0	96.5	105.0	104.0	44.0	18.0	0.0	44.0	999.0	999.0	999.0	0.0	3.0	6.0	3.0	5.0	11.0	6.0	9.0	6.0	9.0	0.0	0.0	0.0	100.0	100.0	36930.0	22808.0	14600.0	6592.0	N	Cash	N	10.0	36.0	2012-11-01	98.0	0	2004-03-01	204.0	68.0	43.0
2103815	121429872	14000.0	14000.0	14000.0	60 months	16.02	340.61	C	C5	MORTGAGE	51600.0	Verified	Nov-2017	Current	n	debt_consolidation	Debt consolidation	945xx	CA	32.84	0.0	Sep-1981	685.0	689.0	1.0	999.0	999.0	16.0	31112.0	53.7	33.0	w	11278.37	11278.37	5437.300000	5437.30	2721.63	2715.67	2019-03-01	340.61	Apr-2019	2019-03-01	654.0	650.0	999.0	1.0	Joint App	0.0	668.0	434780.0	2.0	2.0	3.0	17.0	24105.0	83.0	1.0	3.0	5831.0	63.0	57900.0	2.0	2.0	10.0	27174.0	9703.0	69.6	149.0	433.0	6.0	3.0	6.0	24.0	999.0	5.0	999.0	0.0	7.0	11.0	8.0	11.0	7.0	13.0	20.0	11.0	16.0	0.0	0.0	4.0	100.0	62.5	467088.0	55217.0	31900.0	29188.0	N	Cash	N	10.0	60.0	2017-11-01	38.0	0	1981-09-01	478.0	22.0	22.0
71343	63357608	7500.0	7500.0	7500.0	36 months	11.49	247.29	B	B5	RENT	37000.0	Verified	Nov-2015	Charged Off	n	credit_card	Credit card refinancing	852xx	AZ	18.07	0.0	Aug-2002	675.0	679.0	1.0	66.0	999.0	14.0	5577.0	90.0	26.0	w	0.00	0.00	6182.620000	6182.62	4928.56	1254.06	2017-12-01	247.29	No Payment Due	2018-09-01	519.0	515.0	68.0	1.0	Individual	0.0	0.0	52923.0	0.0	0.0	0.0	999.0	0.0	72.0	0.0	0.0	0.0	58.0	6200.0	0.0	0.0	5.0	4071.0	454.0	91.7	159.0	107.0	3.0	3.0	0.0	3.0	999.0	3.0	999.0	10.0	2.0	3.0	3.0	3.0	21.0	4.0	5.0	3.0	14.0	0.0	0.0	2.0	57.7	100.0	55298.0	52923.0	5500.0	49098.0	N	Cash	N	5.0	36.0	2015-11-01	62.0	1	2002-08-01	223.0	38.0	28.0

	loan_amnt	funded_amnt	funded_amnt_inv	int_rate	installment	grade	home_ownership	annual_inc	verification_status	purpose	addr_state	dti	delinq_2yrs	fico_range_low	fico_range_high	inq_last_6mths	mths_since_last_delinq	mths_since_last_record	open_acc	revol_bal	revol_util	total_acc	initial_list_status	total_pymnt	total_pymnt_inv	total_rec_prncp	total_rec_int	recoveries	collection_recovery_fee	last_pymnt_amnt	last_fico_range_high	last_fico_range_low	mths_since_last_major_derog	policy_code	application_type	tot_coll_amt	tot_cur_bal	open_acc_6m	open_act_il	open_il_12m	open_il_24m	mths_since_rcnt_il	total_bal_il	il_util	open_rv_12m	open_rv_24m	max_bal_bc	all_util	total_rev_hi_lim	total_cu_tl	inq_last_12m	acc_open_past_24mths	avg_cur_bal	bc_open_to_buy	bc_util	mo_sin_old_il_acct	mo_sin_old_rev_tl_op	mo_sin_rcnt_rev_tl_op	mo_sin_rcnt_tl	mths_since_recent_bc	mths_since_recent_bc_dlq	mths_since_recent_inq	mths_since_recent_revol_delinq	num_accts_ever_120_pd	num_actv_bc_tl	num_actv_rev_tl	num_bc_sats	num_bc_tl	num_il_tl	num_op_rev_tl	num_rev_accts	num_rev_tl_bal_gt_0	num_sats	num_tl_90g_dpd_24m	num_tl_op_past_12m	pct_tl_nvr_dlq	percent_bc_gt_75	tot_hi_cred_lim	total_bal_ex_mort	total_bc_limit	total_il_high_credit_limit	hardship_flag	disbursement_method	debt_settlement_flag	emp_length_int	term_int	mths_since_issue_d	mths_since_earliest_cr_line	months_since_last_pymnt	months_since_last_credit_pull	grade:A	grade:B	grade:C	grade:D	grade:E	grade:F	grade:G
299291	12000.0	12000.0	12000.0	13.99	279.16	C	OWN	30000.0	Source Verified	debt_consolidation	SD	25.32	0.0	675.0	679.0	1.0	76.0	999.0	19.0	11405.0	60.3	35.0	w	13667.840000	13667.84	12000.00	1667.84	0.00	0.0000	10615.73	574.0	570.0	999.0	1.0	Individual	0.0	88510.0	0.0	0.0	0.0	0.0	999.0	0.0	72.0	0.0	0.0	0.0	58.0	18900.0	0.0	0.0	4.0	4658.0	1221.0	83.3	127.0	121.0	15.0	5.0	36.0	76.0	5.0	76.0	0.0	6.0	11.0	6.0	14.0	7.0	14.0	28.0	11.0	19.0	0.0	1.0	97.1	66.7	97351.0	88510.0	7300.0	78451.0	N	Cash	N	7.0	60.0	68.0	198.0	57.0	22.0	False	False	True	False	False	False	False
2099335	35000.0	35000.0	35000.0	18.06	889.92	D	RENT	140000.0	Source Verified	debt_consolidation	CA	20.49	0.0	695.0	699.0	2.0	999.0	999.0	12.0	30808.0	20.0	18.0	w	11582.320000	11582.32	3063.09	3986.04	4533.19	815.9742	889.92	574.0	570.0	999.0	1.0	Individual	0.0	91853.0	3.0	4.0	4.0	4.0	4.0	61045.0	87.0	2.0	5.0	11140.0	20.0	157000.0	2.0	4.0	9.0	7654.0	19625.0	20.0	93.0	72.0	3.0	3.0	3.0	999.0	3.0	999.0	0.0	8.0	8.0	8.0	8.0	9.0	8.0	9.0	8.0	12.0	0.0	6.0	100.0	0.0	227204.0	91853.0	157000.0	70204.0	N	Cash	N	2.0	60.0	38.0	132.0	30.0	24.0	False	False	False	True	False	False	False
113647	8400.0	8400.0	8400.0	12.29	280.17	C	MORTGAGE	70495.0	Verified	other	GA	16.04	0.0	660.0	664.0	1.0	44.0	999.0	19.0	16940.0	94.1	39.0	w	10008.154816	10008.15	8400.00	1608.15	0.00	0.0000	196.31	709.0	705.0	45.0	1.0	Individual	79.0	145252.0	0.0	0.0	0.0	0.0	999.0	0.0	72.0	0.0	0.0	0.0	58.0	18000.0	0.0	0.0	8.0	7645.0	207.0	98.0	98.0	101.0	3.0	3.0	16.0	44.0	3.0	44.0	2.0	2.0	5.0	2.0	3.0	28.0	5.0	10.0	5.0	19.0	0.0	2.0	92.1	100.0	142744.0	145252.0	10500.0	124664.0	N	Cash	N	2.0	36.0	63.0	165.0	28.0	28.0	False	False	True	False	False	False	False
180785	18000.0	18000.0	18000.0	7.89	563.15	A	RENT	68000.0	Not Verified	debt_consolidation	NV	11.12	0.0	705.0	709.0	0.0	81.0	999.0	6.0	7540.0	55.4	21.0	w	20235.797565	20235.80	18000.00	2235.80	0.00	0.0000	2230.78	714.0	710.0	999.0	1.0	Individual	0.0	33713.0	0.0	0.0	0.0	0.0	999.0	0.0	72.0	0.0	0.0	0.0	58.0	13600.0	0.0	0.0	2.0	5619.0	4353.0	63.1	94.0	96.0	9.0	9.0	9.0	81.0	21.0	81.0	0.0	3.0	4.0	3.0	5.0	14.0	4.0	7.0	4.0	6.0	0.0	1.0	94.7	33.3	46695.0	33713.0	11800.0	33095.0	N	Cash	N	0.0	36.0	65.0	162.0	32.0	27.0	True	False	False	False	False	False	False
1805875	12000.0	12000.0	12000.0	21.60	455.81	E	RENT	165000.0	Not Verified	credit_card	NY	7.53	1.0	680.0	684.0	3.0	10.0	999.0	12.0	7883.0	47.2	25.0	f	13956.129946	13956.13	12000.00	1956.13	0.00	0.0000	9854.71	749.0	745.0	10.0	1.0	Individual	0.0	26652.0	0.0	0.0	0.0	0.0	999.0	0.0	72.0	0.0	0.0	0.0	58.0	16700.0	0.0	0.0	10.0	2221.0	327.0	85.1	22.0	162.0	2.0	2.0	2.0	34.0	0.0	34.0	1.0	2.0	7.0	2.0	11.0	2.0	9.0	22.0	7.0	12.0	1.0	5.0	86.0	100.0	42613.0	26652.0	2200.0	16500.0	N	Cash	N	2.0	36.0	88.0	251.0	79.0	22.0	False	False	False	False	True	False	False

	purpose	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	wedding	456	0.133772	0.001663	61.0	395.0	0.001037	0.001834	-0.569542	NaN	NaN	0.019054
1	car	2922	0.148186	0.010655	433.0	2489.0	0.007364	0.011554	-0.450429	0.014414	0.119113	0.019054
2	educational	66	0.181818	0.000241	12.0	54.0	0.000204	0.000251	-0.205608	0.033632	0.244821	0.019054
3	credit_card	59492	0.182024	0.216939	10829.0	48663.0	0.184160	0.225886	-0.204222	0.000206	0.001386	0.019054
4	home_improvement	17962	0.195246	0.065499	3507.0	14455.0	0.059641	0.067098	-0.117810	0.013221	0.086412	0.019054
5	major_purchase	6053	0.198744	0.022072	1203.0	4850.0	0.020458	0.022513	-0.095691	0.003499	0.022119	0.019054
6	vacation	1864	0.217275	0.006797	405.0	1459.0	0.006888	0.006772	0.016850	0.018530	0.112541	0.019054
7	debt_consolidation	159235	0.226244	0.580654	36026.0	123209.0	0.612666	0.571916	0.068828	0.008970	0.051978	0.019054
8	other	16206	0.228372	0.059096	3701.0	12505.0	0.062940	0.058046	0.080944	0.002128	0.012116	0.019054
9	moving	2014	0.237339	0.007344	478.0	1536.0	0.008129	0.007130	0.131143	0.008966	0.050199	0.019054
10	medical	3246	0.240604	0.011837	781.0	2465.0	0.013282	0.011442	0.149098	0.003265	0.017954	0.019054
11	house	1477	0.247800	0.005386	366.0	1111.0	0.006224	0.005157	0.188087	0.007196	0.038989	0.019054
12	renewable_energy	173	0.254335	0.000631	44.0	129.0	0.000748	0.000599	0.222847	0.006536	0.034760	0.019054
13	small_business	3068	0.311604	0.011188	956.0	2112.0	0.016258	0.009804	0.505837	0.057268	0.282990	0.019054

	term_int	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	36.0	206971	0.171783	0.754724	35554.0	171417.0	0.604639	0.79569	0.565253	NaN	NaN	0.09772
1	60.0	67263	0.345628	0.245276	23248.0	44015.0	0.395361	0.20431	1.076741	0.173846	0.511488	0.09772

	num_tl_120dpd_2m	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	0.0	274036	0.214435	0.999278	58763.0	215273.0	0.999337	0.999262	0.693185	NaN	NaN	0.000024
1	1.0	191	0.204188	0.000696	39.0	152.0	0.000663	0.000706	0.662701	0.010247	0.030484	0.000024
2	2.0	7	0.000000	0.000026	0.0	7.0	0.000000	0.000032	0.000000	0.204188	0.662701	0.000024

	num_tl_30dpd	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	0.0	273429	0.214330	0.997065	58604.0	214825.0	0.996633	0.997182	0.692872	NaN	NaN	0.000058
1	1.0	761	0.248357	0.002775	189.0	572.0	0.003214	0.002655	0.793243	0.034028	0.100371	0.000058
2	2.0	37	0.216216	0.000135	8.0	29.0	0.000136	0.000135	0.698469	0.032141	0.094774	0.000058
3	3.0	7	0.142857	0.000026	1.0	6.0	0.000017	0.000028	0.476616	0.073359	0.221853	0.000058

	delinq_record_risk_score	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	0	256999	0.213304	0.937152	54819.0	202180.0	0.932264	0.938486	0.689827	NaN	NaN	0.00046
1	1	13401	0.230505	0.048867	3089.0	10312.0	0.052532	0.047867	0.740732	0.017201	0.050906	0.00046
2	2	1588	0.229849	0.005791	365.0	1223.0	0.006207	0.005677	0.738796	0.000656	0.001936	0.00046
3	3	987	0.245187	0.003599	242.0	745.0	0.004116	0.003458	0.783939	0.015339	0.045143	0.00046
4	4	1166	0.237564	0.004252	277.0	889.0	0.004711	0.004127	0.761531	0.007623	0.022408	0.00046
5	5	69	0.086957	0.000252	6.0	63.0	0.000102	0.000292	0.299306	0.150608	0.462225	0.00046
6	6	13	0.076923	0.000047	1.0	12.0	0.000017	0.000056	0.266438	0.010033	0.032868	0.00046
7	7	11	0.272727	0.000040	3.0	8.0	0.000051	0.000037	0.864527	0.195804	0.598088	0.00046

	loan_amnt_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(460.5, 1290.0]	1742	0.128588	0.006352	224.0	1518.0	0.003809	0.007046	0.432187	NaN	NaN	0.022676
1	(1290.0, 2080.0]	3896	0.154517	0.014207	602.0	3294.0	0.010238	0.015290	0.512562	0.025930	0.080375	0.022676
2	(2080.0, 2870.0]	3583	0.149595	0.013065	536.0	3047.0	0.009115	0.014144	0.497425	0.004922	0.015136	0.022676
3	(2870.0, 3660.0]	7864	0.173194	0.028676	1362.0	6502.0	0.023162	0.030181	0.569536	0.023599	0.072111	0.022676
4	(3660.0, 4450.0]	6291	0.175648	0.022940	1105.0	5186.0	0.018792	0.024073	0.576970	0.002453	0.007434	0.022676
5	(4450.0, 5240.0]	14267	0.177402	0.052025	2531.0	11736.0	0.043043	0.054477	0.582280	0.001755	0.005310	0.022676
6	(5240.0, 6030.0]	13689	0.165827	0.049917	2270.0	11419.0	0.038604	0.053005	0.547144	0.011576	0.035136	0.022676
7	(6030.0, 6820.0]	4791	0.173868	0.017470	833.0	3958.0	0.014166	0.018372	0.571577	0.008041	0.024434	0.022676
8	(6820.0, 7610.0]	10612	0.168300	0.038697	1786.0	8826.0	0.030373	0.040969	0.554673	0.005568	0.016905	0.022676
9	(7610.0, 8400.0]	13440	0.180283	0.049009	2423.0	11017.0	0.041206	0.051139	0.590984	0.011983	0.036311	0.022676
10	(8400.0, 9190.0]	6964	0.170017	0.025394	1184.0	5780.0	0.020135	0.026830	0.559893	0.010266	0.031091	0.022676
11	(9190.0, 9980.0]	5397	0.201223	0.019680	1086.0	4311.0	0.018469	0.020011	0.653851	0.031206	0.093958	0.022676
12	(9980.0, 10770.0]	23726	0.210613	0.086517	4997.0	18729.0	0.084980	0.086937	0.681829	0.009390	0.027978	0.022676
13	(10770.0, 11560.0]	6664	0.230642	0.024300	1537.0	5127.0	0.026139	0.023799	0.741137	0.020029	0.059308	0.022676
14	(11560.0, 12350.0]	16752	0.211736	0.061087	3547.0	13205.0	0.060321	0.061295	0.685167	0.018906	0.055969	0.022676
15	(12350.0, 13140.0]	5349	0.217798	0.019505	1165.0	4184.0	0.019812	0.019421	0.703158	0.006062	0.017991	0.022676
16	(13140.0, 13930.0]	2814	0.264748	0.010261	745.0	2069.0	0.012670	0.009604	0.841227	0.046950	0.138068	0.022676
17	(13930.0, 14720.0]	7764	0.231968	0.028312	1801.0	5963.0	0.030628	0.027679	0.745047	0.032780	0.096180	0.022676
18	(14720.0, 15510.0]	16292	0.221581	0.059409	3610.0	12682.0	0.061392	0.058868	0.714364	0.010387	0.030682	0.022676
19	(15510.0, 16300.0]	9284	0.243968	0.033854	2265.0	7019.0	0.038519	0.032581	0.780359	0.022387	0.065994	0.022676
20	(16300.0, 17090.0]	4042	0.239485	0.014739	968.0	3074.0	0.016462	0.014269	0.767183	0.004483	0.013175	0.022676
21	(17090.0, 17880.0]	2133	0.280825	0.007778	599.0	1534.0	0.010187	0.007121	0.888140	0.041340	0.120957	0.022676
22	(17880.0, 18670.0]	7859	0.236035	0.028658	1855.0	6004.0	0.031547	0.027870	0.757030	0.044790	0.131110	0.022676
23	(18670.0, 19460.0]	3120	0.267949	0.011377	836.0	2284.0	0.014217	0.010602	0.850578	0.031914	0.093548	0.022676
24	(19460.0, 20250.0]	16121	0.238323	0.058786	3842.0	12279.0	0.065338	0.056997	0.763763	0.029626	0.086815	0.022676
25	(20250.0, 21040.0]	4870	0.244148	0.017759	1189.0	3681.0	0.020220	0.017087	0.780887	0.005825	0.017124	0.022676
26	(21040.0, 21830.0]	1549	0.300194	0.005648	465.0	1084.0	0.007908	0.005032	0.944528	0.056046	0.163641	0.022676
27	(21830.0, 22620.0]	2759	0.246829	0.010061	681.0	2078.0	0.011581	0.009646	0.788757	0.053365	0.155771	0.022676
28	(22620.0, 23410.0]	1818	0.246975	0.006629	449.0	1369.0	0.007636	0.006355	0.789186	0.000146	0.000429	0.022676
29	(23410.0, 24200.0]	7816	0.236566	0.028501	1849.0	5967.0	0.031445	0.027698	0.758593	0.010409	0.030593	0.022676
30	(24200.0, 24990.0]	1128	0.275709	0.004113	311.0	817.0	0.005289	0.003792	0.873225	0.039143	0.114632	0.022676
31	(24990.0, 25780.0]	7941	0.227049	0.028957	1803.0	6138.0	0.030662	0.028492	0.730532	0.048660	0.142693	0.022676
32	(25780.0, 26570.0]	1337	0.257292	0.004875	344.0	993.0	0.005850	0.004609	0.819424	0.030243	0.088892	0.022676
33	(26570.0, 27360.0]	1162	0.274527	0.004237	319.0	843.0	0.005425	0.003913	0.869776	0.017234	0.050352	0.022676
34	(27360.0, 28150.0]	4685	0.210672	0.017084	987.0	3698.0	0.016785	0.017166	0.682006	0.063854	0.187770	0.022676
35	(28150.0, 28940.0]	689	0.256894	0.002512	177.0	512.0	0.003010	0.002377	0.818258	0.046222	0.136252	0.022676
36	(28940.0, 29730.0]	821	0.258222	0.002994	212.0	609.0	0.003605	0.002827	0.822143	0.001328	0.003886	0.022676
37	(29730.0, 30520.0]	6437	0.275128	0.023473	1771.0	4666.0	0.030118	0.021659	0.871531	0.016906	0.049387	0.022676
38	(30520.0, 31310.0]	644	0.296584	0.002348	191.0	453.0	0.003248	0.002103	0.934026	0.021456	0.062495	0.022676
39	(31310.0, 32100.0]	1569	0.284895	0.005721	447.0	1122.0	0.007602	0.005208	0.899997	0.011689	0.034028	0.022676
40	(32100.0, 32890.0]	476	0.319328	0.001736	152.0	324.0	0.002585	0.001504	1.000178	0.034433	0.100181	0.022676
41	(32890.0, 33680.0]	771	0.258106	0.002811	199.0	572.0	0.003384	0.002655	0.821806	0.061221	0.178372	0.022676
42	(33680.0, 34470.0]	382	0.324607	0.001393	124.0	258.0	0.002109	0.001198	1.015535	0.066501	0.193729	0.022676
43	(34470.0, 35260.0]	10860	0.262523	0.039601	2851.0	8009.0	0.048485	0.037176	0.834724	0.062084	0.180811	0.022676
44	(35260.0, 36050.0]	329	0.273556	0.001200	90.0	239.0	0.001531	0.001109	0.866945	0.011033	0.032221	0.022676
45	(36050.0, 36840.0]	35	0.257143	0.000128	9.0	26.0	0.000153	0.000121	0.818986	0.016413	0.047959	0.022676
46	(36840.0, 37630.0]	53	0.207547	0.000193	11.0	42.0	0.000187	0.000195	0.672708	0.049596	0.146278	0.022676
47	(37630.0, 38420.0]	72	0.250000	0.000263	18.0	54.0	0.000306	0.000251	0.798060	0.042453	0.125352	0.022676
48	(38420.0, 39210.0]	37	0.216216	0.000135	8.0	29.0	0.000136	0.000135	0.698469	0.033784	0.099591	0.022676
49	(39210.0, 40000.0]	1538	0.283485	0.005608	436.0	1102.0	0.007415	0.005115	0.895890	0.067269	0.197422	0.022676

	int_rate_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(5.284, 5.824]	6219	0.039878	0.022678	248.0	5971.0	0.004218	0.027716	0.141645	NaN	NaN	0.209145
1	(5.824, 6.337]	4802	0.036443	0.017511	175.0	4627.0	0.002976	0.021478	0.129770	0.003435	0.011876	0.209145
2	(6.337, 6.851]	4886	0.055055	0.017817	269.0	4617.0	0.004575	0.021431	0.193473	0.018612	0.063704	0.209145
3	(6.851, 7.364]	9903	0.070181	0.036111	695.0	9208.0	0.011819	0.042742	0.244143	0.015125	0.050670	0.209145
4	(7.364, 7.878]	6047	0.078551	0.022051	475.0	5572.0	0.008078	0.025864	0.271797	0.008371	0.027654	0.209145
5	(7.878, 8.392]	18415	0.100081	0.067151	1843.0	16572.0	0.031342	0.076925	0.341776	0.021530	0.069979	0.209145
6	(8.392, 8.905]	4307	0.088925	0.015706	383.0	3924.0	0.006513	0.018215	0.305713	0.011156	0.036063	0.209145
7	(8.905, 9.419]	7881	0.118513	0.028738	934.0	6947.0	0.015884	0.032247	0.400499	0.029588	0.094787	0.209145
8	(9.419, 9.932]	9372	0.143833	0.034175	1348.0	8024.0	0.022924	0.037246	0.479635	0.025320	0.079136	0.209145
9	(9.932, 10.446]	8276	0.137869	0.030179	1141.0	7135.0	0.019404	0.033119	0.461140	0.005964	0.018494	0.209145
10	(10.446, 10.96]	8089	0.173445	0.029497	1403.0	6686.0	0.023860	0.031035	0.570297	0.035577	0.109157	0.209145
11	(10.96, 11.473]	14962	0.154458	0.054559	2311.0	12651.0	0.039301	0.058724	0.512379	0.018987	0.057919	0.209145
12	(11.473, 11.987]	10049	0.171161	0.036644	1720.0	8329.0	0.029251	0.038662	0.563368	0.016703	0.050989	0.209145
13	(11.987, 12.5]	15824	0.173913	0.057703	2752.0	13072.0	0.046801	0.060678	0.571715	0.002752	0.008347	0.209145
14	(12.5, 13.014]	15417	0.214893	0.056218	3313.0	12104.0	0.056342	0.056185	0.694542	0.040980	0.122827	0.209145
15	(13.014, 13.528]	10572	0.209894	0.038551	2219.0	8353.0	0.037737	0.038773	0.679692	0.004999	0.014850	0.209145
16	(13.528, 14.041]	14814	0.239503	0.054020	3548.0	11266.0	0.060338	0.052295	0.767236	0.029609	0.087544	0.209145
17	(14.041, 14.555]	10999	0.240204	0.040108	2642.0	8357.0	0.044930	0.038792	0.769296	0.000700	0.002060	0.209145
18	(14.555, 15.068]	9532	0.267520	0.034759	2550.0	6982.0	0.043366	0.032409	0.849325	0.027316	0.080030	0.209145
19	(15.068, 15.582]	4554	0.228371	0.016606	1040.0	3514.0	0.017686	0.016311	0.734433	0.039149	0.114892	0.209145
20	(15.582, 16.096]	12273	0.280535	0.044754	3443.0	8830.0	0.058552	0.040987	0.887293	0.052164	0.152860	0.209145
21	(16.096, 16.609]	7214	0.285694	0.026306	2061.0	5153.0	0.035050	0.023919	0.902326	0.005160	0.015033	0.209145
22	(16.609, 17.123]	6386	0.313811	0.023287	2004.0	4382.0	0.034080	0.020341	0.984135	0.028117	0.081808	0.209145
23	(17.123, 17.636]	6197	0.306923	0.022597	1902.0	4295.0	0.032346	0.019937	0.964101	0.006889	0.020034	0.209145
24	(17.636, 18.15]	5692	0.345397	0.020756	1966.0	3726.0	0.033434	0.017295	1.076067	0.038474	0.111966	0.209145
25	(18.15, 18.664]	6027	0.338477	0.021978	2040.0	3987.0	0.034693	0.018507	1.055904	0.006920	0.020163	0.209145
26	(18.664, 19.177]	5708	0.345480	0.020814	1972.0	3736.0	0.033536	0.017342	1.076309	0.007003	0.020405	0.209145
27	(19.177, 19.691]	3499	0.368963	0.012759	1291.0	2208.0	0.021955	0.010249	1.144900	0.023483	0.068592	0.209145
28	(19.691, 20.204]	4606	0.382327	0.016796	1761.0	2845.0	0.029948	0.013206	1.184102	0.013365	0.039202	0.209145
29	(20.204, 20.718]	1330	0.328571	0.004850	437.0	893.0	0.007432	0.004145	1.027069	0.053756	0.157033	0.209145
30	(20.718, 21.232]	2649	0.392601	0.009660	1040.0	1609.0	0.017686	0.007469	1.214341	0.064030	0.187273	0.209145
31	(21.232, 21.745]	2271	0.393219	0.008281	893.0	1378.0	0.015187	0.006396	1.216163	0.000618	0.001822	0.209145
32	(21.745, 22.259]	1727	0.413434	0.006298	714.0	1013.0	0.012142	0.004702	1.276005	0.020215	0.059842	0.209145
33	(22.259, 22.772]	1798	0.397108	0.006556	714.0	1084.0	0.012142	0.005032	1.227640	0.016326	0.048365	0.209145
34	(22.772, 23.286]	1345	0.439405	0.004905	591.0	754.0	0.010051	0.003500	1.353685	0.042297	0.126045	0.209145
35	(23.286, 23.8]	894	0.359060	0.003260	321.0	573.0	0.005459	0.002660	1.115938	0.080345	0.237747	0.209145
36	(23.8, 24.313]	1836	0.456972	0.006695	839.0	997.0	0.014268	0.004628	1.406852	0.097911	0.290914	0.209145
37	(24.313, 24.827]	1043	0.410355	0.003803	428.0	615.0	0.007279	0.002855	1.266859	0.046617	0.139993	0.209145
38	(24.827, 25.34]	1562	0.484635	0.005696	757.0	805.0	0.012874	0.003737	1.491831	0.074280	0.224972	0.209145
39	(25.34, 25.854]	1367	0.455011	0.004985	622.0	745.0	0.010578	0.003458	1.400889	0.029624	0.090942	0.209145
40	(25.854, 26.368]	1081	0.474561	0.003942	513.0	568.0	0.008724	0.002637	1.460689	0.019550	0.059799	0.209145
41	(26.368, 26.881]	362	0.569061	0.001320	206.0	156.0	0.003503	0.000724	1.764378	0.094500	0.303690	0.209145
42	(26.881, 27.395]	300	0.493333	0.001094	148.0	152.0	0.002517	0.000706	1.518916	0.075727	0.245462	0.209145
43	(27.395, 27.908]	186	0.612903	0.000678	114.0	72.0	0.001939	0.000334	1.917045	0.119570	0.398129	0.209145
44	(27.908, 28.422]	114	0.535088	0.000416	61.0	53.0	0.001037	0.000246	1.651864	0.077816	0.265181	0.209145
45	(28.422, 28.936]	452	0.522124	0.001648	236.0	216.0	0.004013	0.001003	1.610021	0.012964	0.041843	0.209145
46	(28.936, 29.449]	52	0.596154	0.000190	31.0	21.0	0.000527	0.000097	1.857594	0.074030	0.247573	0.209145
47	(29.449, 29.963]	248	0.552419	0.000904	137.0	111.0	0.002330	0.000515	1.708712	0.043734	0.148881	0.209145
48	(29.963, 30.476]	228	0.429825	0.000831	98.0	130.0	0.001667	0.000603	1.324912	0.122595	0.383800	0.209145
49	(30.476, 30.99]	867	0.522491	0.003162	453.0	414.0	0.007704	0.001922	1.611199	0.092667	0.286287	0.209145

	annual_inc_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(-140.0, 2800.0]	83	0.240964	0.000325	20.0	63.0	0.000360	0.000316	0.760526	NaN	NaN	0.010553
1	(2800.0, 5600.0]	31	0.354839	0.000122	11.0	20.0	0.000198	0.000100	1.089912	0.113875	0.329386	0.010553
2	(5600.0, 8400.0]	78	0.269231	0.000306	21.0	57.0	0.000378	0.000286	0.842560	0.085608	0.247352	0.010553
3	(8400.0, 11200.0]	350	0.291429	0.001372	102.0	248.0	0.001835	0.001243	0.906712	0.022198	0.064152	0.010553
4	(11200.0, 14000.0]	647	0.281298	0.002537	182.0	465.0	0.003275	0.002331	0.877455	0.010130	0.029257	0.010553
5	(14000.0, 16800.0]	1014	0.279093	0.003976	283.0	731.0	0.005092	0.003665	0.871081	0.002206	0.006374	0.010553
6	(16800.0, 19600.0]	1285	0.257588	0.005038	331.0	954.0	0.005956	0.004783	0.808830	0.021505	0.062251	0.010553
7	(19600.0, 22400.0]	2672	0.264222	0.010477	706.0	1966.0	0.012704	0.009856	0.828057	0.006634	0.019227	0.010553
8	(22400.0, 25200.0]	4492	0.255120	0.017613	1146.0	3346.0	0.020621	0.016775	0.801672	0.009101	0.026385	0.010553
9	(25200.0, 28000.0]	3879	0.260376	0.015209	1010.0	2869.0	0.018174	0.014383	0.816916	0.005256	0.015243	0.010553
10	(28000.0, 30800.0]	6069	0.245840	0.023796	1492.0	4577.0	0.026847	0.022946	0.774714	0.014537	0.042202	0.010553
11	(30800.0, 33600.0]	5768	0.248440	0.022616	1433.0	4335.0	0.025785	0.021733	0.782273	0.002600	0.007559	0.010553
12	(33600.0, 36400.0]	9387	0.251838	0.036806	2364.0	7023.0	0.042537	0.035209	0.792144	0.003398	0.009871	0.010553
13	(36400.0, 39200.0]	6374	0.238783	0.024992	1522.0	4852.0	0.027386	0.024325	0.754172	0.013055	0.037972	0.010553
14	(39200.0, 42000.0]	13875	0.234739	0.054403	3257.0	10618.0	0.058605	0.053232	0.742383	0.004044	0.011789	0.010553
15	(42000.0, 44800.0]	5049	0.246583	0.019797	1245.0	3804.0	0.022402	0.019071	0.776877	0.011845	0.034494	0.010553
16	(44800.0, 47600.0]	11615	0.232114	0.045542	2696.0	8919.0	0.048511	0.044715	0.734722	0.014470	0.042155	0.010553
17	(47600.0, 50400.0]	15763	0.232443	0.061806	3664.0	12099.0	0.065929	0.060657	0.735684	0.000329	0.000962	0.010553
18	(50400.0, 53200.0]	8349	0.219907	0.032736	1836.0	6513.0	0.033036	0.032652	0.699011	0.012536	0.036673	0.010553
19	(53200.0, 56000.0]	11837	0.225395	0.046412	2668.0	9169.0	0.048007	0.045968	0.715086	0.005488	0.016074	0.010553
20	(56000.0, 58800.0]	5150	0.220194	0.020193	1134.0	4016.0	0.020405	0.020134	0.699855	0.005201	0.015231	0.010553
21	(58800.0, 61600.0]	13673	0.224823	0.053611	3074.0	10599.0	0.055313	0.053137	0.713411	0.004628	0.013556	0.010553
22	(61600.0, 64400.0]	6577	0.216360	0.025788	1423.0	5154.0	0.025605	0.025839	0.688607	0.008463	0.024804	0.010553
23	(64400.0, 67200.0]	11622	0.223714	0.045569	2600.0	9022.0	0.046784	0.045231	0.710165	0.007354	0.021558	0.010553
24	(67200.0, 70000.0]	11659	0.217343	0.045714	2534.0	9125.0	0.045596	0.045747	0.691492	0.006371	0.018673	0.010553
25	(70000.0, 72800.0]	4992	0.199319	0.019573	995.0	3997.0	0.017904	0.020039	0.638407	0.018024	0.053085	0.010553
26	(72800.0, 75600.0]	10040	0.213546	0.039366	2144.0	7896.0	0.038578	0.039586	0.680341	0.014227	0.041934	0.010553
27	(75600.0, 78400.0]	4336	0.200876	0.017001	871.0	3465.0	0.015673	0.017371	0.643010	0.012669	0.037331	0.010553
28	(78400.0, 81200.0]	9345	0.209524	0.036641	1958.0	7387.0	0.035232	0.037034	0.668512	0.008647	0.025502	0.010553
29	(81200.0, 84000.0]	4429	0.191465	0.017366	848.0	3581.0	0.015259	0.017953	0.615143	0.018058	0.053369	0.010553
30	(84000.0, 86800.0]	6724	0.206425	0.026364	1388.0	5336.0	0.024975	0.026752	0.659384	0.014959	0.044240	0.010553
31	(86800.0, 89600.0]	3212	0.190224	0.012594	611.0	2601.0	0.010994	0.013040	0.611458	0.016201	0.047925	0.010553
32	(89600.0, 92400.0]	7977	0.193180	0.031277	1541.0	6436.0	0.027728	0.032266	0.620231	0.002956	0.008773	0.010553
33	(92400.0, 95200.0]	4984	0.186798	0.019542	931.0	4053.0	0.016752	0.020319	0.601274	0.006383	0.018957	0.010553
34	(95200.0, 98000.0]	3475	0.195108	0.013625	678.0	2797.0	0.012200	0.014023	0.625944	0.008310	0.024670	0.010553
35	(98000.0, 100800.0]	6710	0.188972	0.026310	1268.0	5442.0	0.022816	0.027283	0.607738	0.006136	0.018206	0.010553
36	(100800.0, 103600.0]	2338	0.165526	0.009167	387.0	1951.0	0.006964	0.009781	0.537625	0.023446	0.070113	0.010553
37	(103600.0, 106400.0]	3469	0.170366	0.013602	591.0	2878.0	0.010634	0.014429	0.552176	0.004840	0.014551	0.010553
38	(106400.0, 109200.0]	1740	0.172989	0.006822	301.0	1439.0	0.005416	0.007214	0.560042	0.002622	0.007866	0.010553
39	(109200.0, 112000.0]	4536	0.189153	0.017785	858.0	3678.0	0.015439	0.018439	0.608278	0.016165	0.048236	0.010553

	emp_length_int	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	0.0	22011	0.221707	0.080264	4880.0	17131.0	0.082990	0.079519	0.714738	NaN	NaN	0.000484
1	1.0	18068	0.223987	0.065885	4047.0	14021.0	0.068824	0.065083	0.721482	0.002280	0.006744	0.000484
2	2.0	24789	0.210698	0.090394	5223.0	19566.0	0.088824	0.090822	0.682083	0.013289	0.039399	0.000484
3	3.0	22148	0.217446	0.080763	4816.0	17332.0	0.081902	0.080452	0.702116	0.006748	0.020033	0.000484
4	4.0	16597	0.216244	0.060521	3589.0	13008.0	0.061035	0.060381	0.698551	0.001202	0.003565	0.000484
5	5.0	17151	0.209550	0.062541	3594.0	13557.0	0.061120	0.062929	0.678670	0.006693	0.019881	0.000484
6	6.0	12651	0.204332	0.046132	2585.0	10066.0	0.043961	0.046725	0.663128	0.005219	0.015542	0.000484
7	7.0	12214	0.202964	0.044539	2479.0	9735.0	0.042158	0.045188	0.659048	0.001368	0.004080	0.000484
8	8.0	12253	0.208276	0.044681	2552.0	9701.0	0.043400	0.045030	0.674876	0.005312	0.015828	0.000484
9	9.0	10294	0.220128	0.037537	2266.0	8028.0	0.038536	0.037265	0.710063	0.011853	0.035187	0.000484
10	10.0	106058	0.214703	0.386743	22771.0	83287.0	0.387249	0.386605	0.693980	0.005425	0.016083	0.000484

	dti_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(-0.04, 20.0]	164391	0.183471	0.602712	30161.0	134230.0	0.51767	0.625813	0.602782	NaN	NaN	0.024369
1	(20.0, 40.0]	108361	0.259337	0.397288	28102.0	80259.0	0.48233	0.374187	0.828119	0.075866	0.225336	0.024369

	min_mths_since_delinquency_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(-0.156, 3.12]	4315	0.221089	0.031104	954.0	3361.0	0.030663	0.031232	0.684007	NaN	NaN	0.001227
1	(3.12, 6.24]	6932	0.242787	0.049969	1683.0	5249.0	0.054095	0.048776	0.746239	0.021698	0.062232	0.001227
2	(6.24, 9.36]	7868	0.235511	0.056716	1853.0	6015.0	0.059559	0.055894	0.725409	0.007276	0.020830	0.001227
3	(9.36, 12.48]	7648	0.231041	0.055130	1767.0	5881.0	0.056795	0.054649	0.712594	0.004470	0.012815	0.001227
4	(12.48, 15.6]	7595	0.228440	0.054748	1735.0	5860.0	0.055766	0.054453	0.705130	0.002601	0.007464	0.001227
5	(15.6, 18.72]	7320	0.223497	0.052766	1636.0	5684.0	0.052584	0.052818	0.690932	0.004942	0.014198	0.001227
6	(18.72, 21.84]	7057	0.223466	0.050870	1577.0	5480.0	0.050688	0.050922	0.690843	0.000031	0.000090	0.001227
7	(21.84, 24.96]	6578	0.223776	0.047417	1472.0	5106.0	0.047313	0.047447	0.691734	0.000310	0.000892	0.001227
8	(24.96, 28.08]	9075	0.225785	0.065416	2049.0	7026.0	0.065859	0.065288	0.697507	0.002009	0.005773	0.001227
9	(28.08, 31.2]	6289	0.223565	0.045334	1406.0	4883.0	0.045192	0.045375	0.691127	0.002220	0.006380	0.001227
10	(31.2, 34.32]	6001	0.221296	0.043258	1328.0	4673.0	0.042684	0.043423	0.684604	0.002269	0.006523	0.001227
11	(34.32, 37.44]	5808	0.217631	0.041866	1264.0	4544.0	0.040627	0.042225	0.674053	0.003666	0.010551	0.001227
12	(37.44, 40.56]	5682	0.228793	0.040958	1300.0	4382.0	0.041785	0.040719	0.706143	0.011162	0.032090	0.001227
13	(40.56, 43.68]	5444	0.216385	0.039243	1178.0	4266.0	0.037863	0.039641	0.670464	0.012408	0.035679	0.001227
14	(43.68, 46.8]	5261	0.224672	0.037923	1182.0	4079.0	0.037992	0.037904	0.694309	0.008287	0.023845	0.001227
15	(46.8, 49.92]	4929	0.218097	0.035530	1075.0	3854.0	0.034553	0.035813	0.675395	0.006575	0.018914	0.001227
16	(49.92, 53.04]	4738	0.225623	0.034153	1069.0	3669.0	0.034360	0.034094	0.697040	0.007526	0.021645	0.001227
17	(53.04, 56.16]	3677	0.220560	0.026505	811.0	2866.0	0.026067	0.026632	0.682486	0.005062	0.014555	0.001227
18	(56.16, 59.28]	3671	0.211114	0.026462	775.0	2896.0	0.024910	0.026911	0.655265	0.009446	0.027221	0.001227
19	(59.28, 62.4]	3379	0.215448	0.024357	728.0	2651.0	0.023399	0.024634	0.667765	0.004334	0.012500	0.001227
20	(62.4, 65.52]	3343	0.227640	0.024098	761.0	2582.0	0.024460	0.023993	0.702834	0.012191	0.035068	0.001227
21	(65.52, 68.64]	3395	0.213844	0.024473	726.0	2669.0	0.023335	0.024801	0.663140	0.013796	0.039694	0.001227
22	(68.64, 71.76]	3173	0.220611	0.022872	700.0	2473.0	0.022499	0.022980	0.682633	0.006768	0.019493	0.001227
23	(71.76, 74.88]	2952	0.226287	0.021279	668.0	2284.0	0.021471	0.021224	0.698949	0.005676	0.016317	0.001227
24	(74.88, 78.0]	3656	0.210613	0.026354	770.0	2886.0	0.024749	0.026818	0.653817	0.015675	0.045132	0.001227
25	(78.0, 81.12]	2322	0.212748	0.016738	494.0	1828.0	0.015878	0.016986	0.659978	0.002135	0.006161	0.001227
26	(81.12, 84.24]	393	0.259542	0.002833	102.0	291.0	0.003278	0.002704	0.794086	0.046794	0.134107	0.001227
27	(84.24, 87.36]	59	0.186441	0.000425	11.0	48.0	0.000354	0.000446	0.583710	0.073101	0.210376	0.001227
28	(87.36, 90.48]	29	0.241379	0.000209	7.0	22.0	0.000225	0.000204	0.742212	0.054939	0.158502	0.001227
29	(90.48, 93.6]	30	0.133333	0.000216	4.0	26.0	0.000129	0.000242	0.426670	0.108046	0.315542	0.001227
30	(93.6, 96.72]	22	0.181818	0.000159	4.0	18.0	0.000129	0.000167	0.570220	0.048485	0.143550	0.001227
31	(96.72, 99.84]	21	0.238095	0.000151	5.0	16.0	0.000161	0.000149	0.732812	0.056277	0.162591	0.001227
32	(99.84, 102.96]	11	0.454545	0.000079	5.0	6.0	0.000161	0.000056	1.356470	0.216450	0.623658	0.001227
33	(102.96, 106.08]	13	0.230769	0.000094	3.0	10.0	0.000096	0.000093	0.711815	0.223776	0.644655	0.001227
34	(106.08, 109.2]	10	0.300000	0.000072	3.0	7.0	0.000096	0.000065	0.909230	0.069231	0.197414	0.001227
35	(109.2, 112.32]	7	0.285714	0.000050	2.0	5.0	0.000064	0.000046	0.868604	0.014286	0.040625	0.001227
36	(112.32, 115.44]	7	0.285714	0.000050	2.0	5.0	0.000064	0.000046	0.868604	0.000000	0.000000	0.001227
37	(115.44, 118.56]	2	0.000000	0.000014	0.0	2.0	0.000000	0.000019	0.000000	0.285714	0.868604	0.001227
38	(118.56, 121.68]	2	0.000000	0.000014	0.0	2.0	0.000000	0.000019	0.000000	0.000000	0.000000	0.001227
39	(121.68, 124.8]	2	0.500000	0.000014	1.0	1.0	0.000032	0.000009	1.494914	0.500000	1.494914	0.001227

	annual_inc_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(-9500.0, 126666.667]	247646	0.219491	0.903046	54356.0	193290.0	0.924390	0.897220	0.708175	NaN	NaN	inf
1	(126666.667, 253333.333]	23822	0.169801	0.086867	4045.0	19777.0	0.068790	0.091802	0.559236	0.049690	0.148939	inf
2	(253333.333, 380000.0]	1839	0.150625	0.006706	277.0	1562.0	0.004711	0.007251	0.500597	0.019176	0.058639	inf
3	(380000.0, 506666.667]	537	0.111732	0.001958	60.0	477.0	0.001020	0.002214	0.379012	0.038893	0.121585	inf
4	(506666.667, 633333.333]	174	0.149425	0.000634	26.0	148.0	0.000442	0.000687	0.496901	0.037693	0.117889	inf
5	(633333.333, 760000.0]	78	0.166667	0.000284	13.0	65.0	0.000221	0.000302	0.549702	0.017241	0.052801	inf
6	(760000.0, 886666.667]	38	0.210526	0.000139	8.0	30.0	0.000136	0.000139	0.681572	0.043860	0.131870	inf
7	(886666.667, 1013333.333]	39	0.179487	0.000142	7.0	32.0	0.000119	0.000149	0.588581	0.031039	0.092990	inf
8	(1013333.333, 1140000.0]	15	0.133333	0.000055	2.0	13.0	0.000034	0.000060	0.447019	0.046154	0.141563	inf
9	(1140000.0, 1266666.667]	9	0.111111	0.000033	1.0	8.0	0.000017	0.000037	0.377039	0.022222	0.069980	inf
10	(1266666.667, 1393333.333]	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	0.111111	0.377039	inf
11	(1393333.333, 1520000.0]	7	0.285714	0.000026	2.0	5.0	0.000034	0.000023	0.902384	0.285714	0.902384	inf
12	(1520000.0, 1646666.667]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
13	(1646666.667, 1773333.333]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	inf
14	(1773333.333, 1900000.0]	3	0.000000	0.000011	0.0	3.0	0.000000	0.000014	0.000000	0.000000	0.000000	inf
15	(1900000.0, 2026666.667]	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	0.000000	0.000000	inf
16	(2026666.667, 2153333.333]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
17	(2153333.333, 2280000.0]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
18	(2280000.0, 2406666.667]	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	NaN	NaN	inf
19	(2406666.667, 2533333.333]	3	0.000000	0.000011	0.0	3.0	0.000000	0.000014	0.000000	0.000000	0.000000	inf
20	(2533333.333, 2660000.0]	1	1.000000	0.000004	1.0	0.0	0.000017	0.000000	inf	1.000000	inf	inf
21	(2660000.0, 2786666.667]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
22	(2786666.667, 2913333.333]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
23	(2913333.333, 3040000.0]	3	0.666667	0.000011	2.0	1.0	0.000034	0.000005	2.119548	NaN	NaN	inf
24	(3040000.0, 3166666.667]	2	0.500000	0.000007	1.0	1.0	0.000017	0.000005	1.539806	0.166667	0.579742	inf
25	(3166666.667, 3293333.333]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
26	(3293333.333, 3420000.0]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
27	(3420000.0, 3546666.667]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
28	(3546666.667, 3673333.333]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
29	(3673333.333, 3800000.0]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
30	(3800000.0, 3926666.667]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	inf
31	(3926666.667, 4053333.333]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
32	(4053333.333, 4180000.0]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
33	(4180000.0, 4306666.667]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
34	(4306666.667, 4433333.333]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
35	(4433333.333, 4560000.0]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
36	(4560000.0, 4686666.667]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	inf
37	(4686666.667, 4813333.333]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.000000	0.000000	inf
38	(4813333.333, 4940000.0]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
39	(4940000.0, 5066666.667]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
40	(5066666.667, 5193333.333]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
41	(5193333.333, 5320000.0]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
42	(5320000.0, 5446666.667]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
43	(5446666.667, 5573333.333]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
44	(5573333.333, 5700000.0]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	inf
45	(5700000.0, 5826666.667]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
46	(5826666.667, 5953333.333]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
47	(5953333.333, 6080000.0]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	inf
48	(6080000.0, 6206666.667]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
49	(6206666.667, 6333333.333]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
50	(6333333.333, 6460000.0]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
51	(6460000.0, 6586666.667]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	inf
52	(6586666.667, 6713333.333]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
53	(6713333.333, 6840000.0]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
54	(6840000.0, 6966666.667]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
55	(6966666.667, 7093333.333]	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	NaN	NaN	inf
56	(7093333.333, 7220000.0]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
57	(7220000.0, 7346666.667]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
58	(7346666.667, 7473333.333]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
59	(7473333.333, 7600000.0]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	inf
60	(7600000.0, 7726666.667]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
61	(7726666.667, 7853333.333]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
62	(7853333.333, 7980000.0]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
63	(7980000.0, 8106666.667]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
64	(8106666.667, 8233333.333]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
65	(8233333.333, 8360000.0]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
66	(8360000.0, 8486666.667]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
67	(8486666.667, 8613333.333]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
68	(8613333.333, 8740000.0]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
69	(8740000.0, 8866666.667]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
70	(8866666.667, 8993333.333]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
71	(8993333.333, 9120000.0]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
72	(9120000.0, 9246666.667]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
73	(9246666.667, 9373333.333]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	inf
74	(9373333.333, 9500000.0]	1	1.000000	0.000004	1.0	0.0	0.000017	0.000000	inf	1.000000	inf	inf

	dti_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(-0.999, 19.98]	164167	0.183502	0.598638	30125.0	134042.0	0.512313	0.622201	0.600696	NaN	NaN	inf
1	(19.98, 39.96]	108557	0.259136	0.395855	28131.0	80426.0	0.478402	0.373324	0.824818	0.075634	0.224122	inf
2	(39.96, 59.94]	1108	0.349278	0.004040	387.0	721.0	0.006581	0.003347	1.087383	0.090142	0.262565	inf
3	(59.94, 79.92]	204	0.392157	0.000744	80.0	124.0	0.001360	0.000576	1.213032	0.042879	0.125649	inf
4	(79.92, 99.9]	76	0.421053	0.000277	32.0	44.0	0.000544	0.000204	1.298691	0.028896	0.085659	inf
5	(99.9, 119.88]	32	0.406250	0.000117	13.0	19.0	0.000221	0.000088	1.254684	0.014803	0.044007	inf
6	(119.88, 139.86]	26	0.538462	0.000095	14.0	12.0	0.000238	0.000056	1.662846	0.132212	0.408161	inf
7	(139.86, 159.84]	5	0.400000	0.000018	2.0	3.0	0.000034	0.000014	1.236185	0.138462	0.426660	inf
8	(159.84, 179.82]	6	0.166667	0.000022	1.0	5.0	0.000017	0.000023	0.549702	0.233333	0.686483	inf
9	(179.82, 199.8]	4	0.000000	0.000015	0.0	4.0	0.000000	0.000019	0.000000	0.166667	0.549702	inf
10	(199.8, 219.78]	5	0.200000	0.000018	1.0	4.0	0.000017	0.000019	0.650199	0.200000	0.650199	inf
11	(219.78, 239.76]	5	0.000000	0.000018	0.0	5.0	0.000000	0.000023	0.000000	0.200000	0.650199	inf
12	(239.76, 259.74]	6	0.666667	0.000022	4.0	2.0	0.000068	0.000009	2.119548	0.666667	2.119548	inf
13	(259.74, 279.72]	3	0.333333	0.000011	1.0	2.0	0.000017	0.000009	1.040928	0.333333	1.078620	inf
14	(279.72, 299.7]	3	0.333333	0.000011	1.0	2.0	0.000017	0.000009	1.040928	0.000000	0.000000	inf
15	(299.7, 319.68]	5	0.200000	0.000018	1.0	4.0	0.000017	0.000019	0.650199	0.133333	0.390729	inf
16	(319.68, 339.66]	2	0.500000	0.000007	1.0	1.0	0.000017	0.000005	1.539806	0.300000	0.889607	inf
17	(339.66, 359.64]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
18	(359.64, 379.62]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	inf
19	(379.62, 399.6]	1	1.000000	0.000004	1.0	0.0	0.000017	0.000000	inf	1.000000	inf	inf
20	(399.6, 419.58]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	1.000000	inf	inf
21	(419.58, 439.56]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
22	(439.56, 459.54]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	inf
23	(459.54, 479.52]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
24	(479.52, 499.5]	1	1.000000	0.000004	1.0	0.0	0.000017	0.000000	inf	NaN	NaN	inf
25	(499.5, 519.48]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
26	(519.48, 539.46]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	inf
27	(539.46, 559.44]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
28	(559.44, 579.42]	1	1.000000	0.000004	1.0	0.0	0.000017	0.000000	inf	NaN	NaN	inf
29	(579.42, 599.4]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
30	(599.4, 619.38]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
31	(619.38, 639.36]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
32	(639.36, 659.34]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
33	(659.34, 679.32]	2	0.500000	0.000007	1.0	1.0	0.000017	0.000005	1.539806	NaN	NaN	inf
34	(679.32, 699.3]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
35	(699.3, 719.28]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
36	(719.28, 739.26]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
37	(739.26, 759.24]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
38	(759.24, 779.22]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	inf
39	(779.22, 799.2]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
40	(799.2, 819.18]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
41	(819.18, 839.16]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
42	(839.16, 859.14]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
43	(859.14, 879.12]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
44	(879.12, 899.1]	1	1.000000	0.000004	1.0	0.0	0.000017	0.000000	inf	NaN	NaN	inf
45	(899.1, 919.08]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
46	(919.08, 939.06]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
47	(939.06, 959.04]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
48	(959.04, 979.02]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
49	(979.02, 999.0]	9	0.333333	0.000033	3.0	6.0	0.000051	0.000028	1.040928	NaN	NaN	inf

	mths_since_earliest_cr_line_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(63.009, 86.022]	828	0.379227	0.003019	314.0	514.0	0.005340	0.002386	1.174995	NaN	NaN	0.014181
1	(86.022, 108.044]	3257	0.312558	0.011877	1018.0	2239.0	0.017312	0.010393	0.980488	0.066669	0.194507	0.014181
2	(108.044, 130.067]	6589	0.274093	0.024027	1806.0	4783.0	0.030713	0.022202	0.868512	0.038464	0.111977	0.014181
3	(130.067, 152.089]	8358	0.249103	0.030478	2082.0	6276.0	0.035407	0.029132	0.795429	0.024991	0.073083	0.014181
4	(152.089, 174.111]	16774	0.247168	0.061167	4146.0	12628.0	0.070508	0.058617	0.789754	0.001934	0.005675	0.014181
5	(174.111, 196.133]	26766	0.247105	0.097603	6614.0	20152.0	0.112479	0.093542	0.789567	0.000064	0.000187	0.014181
6	(196.133, 218.156]	31526	0.231618	0.114960	7302.0	24224.0	0.124179	0.112444	0.744016	0.015486	0.045551	0.014181
7	(218.156, 240.178]	33108	0.211278	0.120729	6995.0	26113.0	0.118959	0.121212	0.683807	0.020340	0.060208	0.014181
8	(240.178, 262.2]	29997	0.203487	0.109385	6104.0	23893.0	0.103806	0.110907	0.660609	0.007791	0.023199	0.014181
9	(262.2, 284.222]	24356	0.199951	0.088815	4870.0	19486.0	0.082820	0.090451	0.650051	0.003536	0.010557	0.014181
10	(284.222, 306.244]	19873	0.201027	0.072467	3995.0	15878.0	0.067940	0.073703	0.653265	0.001076	0.003214	0.014181
11	(306.244, 328.267]	16852	0.188998	0.061451	3185.0	13667.0	0.054165	0.063440	0.617236	0.012028	0.036029	0.014181
12	(328.267, 350.289]	12111	0.189910	0.044163	2300.0	9811.0	0.039114	0.045541	0.619974	0.000912	0.002739	0.014181
13	(350.289, 372.311]	9398	0.185359	0.034270	1742.0	7656.0	0.029625	0.035538	0.606288	0.004551	0.013686	0.014181
14	(372.311, 394.333]	8182	0.180640	0.029836	1478.0	6704.0	0.025135	0.031119	0.592064	0.004718	0.014224	0.014181
15	(394.333, 416.356]	6746	0.189149	0.024599	1276.0	5470.0	0.021700	0.025391	0.617689	0.008509	0.025625	0.014181
16	(416.356, 438.378]	5158	0.184762	0.018809	953.0	4205.0	0.016207	0.019519	0.604490	0.004388	0.013198	0.014181
17	(438.378, 460.4]	4077	0.170223	0.014867	694.0	3383.0	0.011802	0.015703	0.560519	0.014538	0.043972	0.014181
18	(460.4, 482.422]	2610	0.167433	0.009517	437.0	2173.0	0.007432	0.010087	0.552035	0.002790	0.008484	0.014181
19	(482.422, 504.444]	1839	0.177814	0.006706	327.0	1512.0	0.005561	0.007018	0.583525	0.010381	0.031490	0.014181
20	(504.444, 526.467]	1718	0.185099	0.006265	318.0	1400.0	0.005408	0.006499	0.605506	0.007285	0.021982	0.014181
21	(526.467, 548.489]	1273	0.186174	0.004642	237.0	1036.0	0.004030	0.004809	0.608744	0.001075	0.003237	0.014181
22	(548.489, 570.511]	813	0.206642	0.002965	168.0	645.0	0.002857	0.002994	0.670013	0.020468	0.061269	0.014181
23	(570.511, 592.533]	623	0.191011	0.002272	119.0	504.0	0.002024	0.002339	0.623281	0.015631	0.046732	0.014181
24	(592.533, 614.556]	436	0.199541	0.001590	87.0	349.0	0.001480	0.001620	0.648828	0.008530	0.025547	0.014181
25	(614.556, 636.578]	338	0.230769	0.001233	78.0	260.0	0.001326	0.001207	0.741511	0.031228	0.092683	0.014181
26	(636.578, 658.6]	270	0.255556	0.000985	69.0	201.0	0.001173	0.000933	0.814339	0.024786	0.072828	0.014181
27	(658.6, 680.622]	141	0.205674	0.000514	29.0	112.0	0.000493	0.000520	0.667128	0.049882	0.147211	0.014181
28	(680.622, 702.644]	101	0.277228	0.000368	28.0	73.0	0.000476	0.000339	0.877653	0.071554	0.210525	0.014181
29	(702.644, 724.667]	47	0.255319	0.000171	12.0	35.0	0.000204	0.000162	0.813647	0.021909	0.064007	0.014181
30	(724.667, 746.689]	28	0.214286	0.000102	6.0	22.0	0.000102	0.000102	0.692740	0.041033	0.120906	0.014181
31	(746.689, 768.711]	18	0.388889	0.000066	7.0	11.0	0.000119	0.000051	1.203403	0.174603	0.510663	0.014181
32	(768.711, 790.733]	11	0.272727	0.000040	3.0	8.0	0.000051	0.000037	0.864527	0.116162	0.338877	0.014181
33	(790.733, 812.756]	3	0.333333	0.000011	1.0	2.0	0.000017	0.000009	1.040928	0.060606	0.176401	0.014181
34	(812.756, 834.778]	4	0.500000	0.000015	2.0	2.0	0.000034	0.000009	1.539806	0.166667	0.498878	0.014181
35	(834.778, 856.8]	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	0.500000	1.539806	0.014181
36	(856.8, 878.822]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.000000	0.000000	0.014181
37	(878.822, 900.844]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.014181
38	(900.844, 922.867]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.014181
39	(922.867, 944.889]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	0.014181
40	(944.889, 966.911]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.014181
41	(966.911, 988.933]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.014181
42	(988.933, 1010.956]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.014181
43	(1010.956, 1032.978]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.014181
44	(1032.978, 1055.0]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	0.014181

	delinq_2yrs	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	0.0	221126	0.210726	0.806341	46597.0	174529.0	0.792439	0.810135	0.682165	NaN	NaN	inf
1	1.0	35193	0.223482	0.128332	7865.0	27328.0	0.133754	0.126852	0.719988	0.012756	0.037823	inf
2	2.0	10331	0.235989	0.037672	2438.0	7893.0	0.041461	0.036638	0.756893	0.012507	0.036905	inf
3	3.0	3763	0.257242	0.013722	968.0	2795.0	0.016462	0.012974	0.819275	0.021253	0.062381	inf
4	4.0	1651	0.245306	0.006020	405.0	1246.0	0.006888	0.005784	0.784287	0.011936	0.034988	inf
5	5.0	870	0.237931	0.003172	207.0	663.0	0.003520	0.003078	0.762610	0.007375	0.021677	inf
6	6.0	503	0.228628	0.001834	115.0	388.0	0.001956	0.001801	0.735194	0.009303	0.027417	inf
7	7.0	295	0.254237	0.001076	75.0	220.0	0.001275	0.001021	0.810478	0.025609	0.075285	inf
8	8.0	158	0.259494	0.000576	41.0	117.0	0.000697	0.000543	0.825865	0.005256	0.015387	inf
9	9.0	114	0.271930	0.000416	31.0	83.0	0.000527	0.000385	0.862200	0.012436	0.036335	inf
10	10.0	72	0.222222	0.000263	16.0	56.0	0.000272	0.000260	0.716262	0.049708	0.145938	inf
11	11.0	44	0.340909	0.000160	15.0	29.0	0.000255	0.000135	1.062988	0.118687	0.346727	inf
12	12.0	31	0.193548	0.000113	6.0	25.0	0.000102	0.000116	0.630891	0.147361	0.432097	inf
13	13.0	25	0.240000	0.000091	6.0	19.0	0.000102	0.000088	0.768697	0.046452	0.137806	inf
14	14.0	19	0.315789	0.000069	6.0	13.0	0.000102	0.000060	0.989887	0.075789	0.221191	inf
15	15.0	13	0.307692	0.000047	4.0	9.0	0.000068	0.000042	0.966339	0.008097	0.023548	inf
16	16.0	6	0.333333	0.000022	2.0	4.0	0.000034	0.000019	1.040928	0.025641	0.074589	inf
17	17.0	5	0.200000	0.000018	1.0	4.0	0.000017	0.000019	0.650199	0.133333	0.390729	inf
18	18.0	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	0.200000	0.650199	inf
19	19.0	2	0.500000	0.000007	1.0	1.0	0.000017	0.000005	1.539806	0.500000	1.539806	inf
20	20.0	5	0.200000	0.000018	1.0	4.0	0.000017	0.000019	0.650199	0.300000	0.889607	inf
21	22.0	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.200000	0.650199	inf
22	24.0	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.000000	0.000000	inf
23	26.0	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	0.000000	0.000000	inf
24	27.0	1	1.000000	0.000004	1.0	0.0	0.000017	0.000000	inf	1.000000	inf	inf
25	36.0	1	1.000000	0.000004	1.0	0.0	0.000017	0.000000	inf	0.000000	NaN	inf

	inq_last_6mths	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	0.0	157016	0.195547	0.572562	30704.0	126312.0	0.522159	0.586320	0.636879	NaN	NaN	0.010856
1	1.0	74667	0.227222	0.272275	16966.0	57701.0	0.288528	0.267839	0.731042	0.031675	0.094163	0.010856
2	2.0	27967	0.253477	0.101982	7089.0	20878.0	0.120557	0.096912	0.808252	0.026255	0.077210	0.010856
3	3.0	10517	0.275554	0.038350	2898.0	7619.0	0.049284	0.035366	0.872772	0.022077	0.064520	0.010856
4	4.0	2849	0.282906	0.010389	806.0	2043.0	0.013707	0.009483	0.894204	0.007352	0.021432	0.010856
5	5.0	1006	0.290258	0.003668	292.0	714.0	0.004966	0.003314	0.915616	0.007352	0.021412	0.010856
6	6.0	200	0.230000	0.000729	46.0	154.0	0.000782	0.000715	0.739242	0.060258	0.176374	0.010856
7	7.0	6	0.166667	0.000022	1.0	5.0	0.000017	0.000023	0.549702	0.063333	0.189540	0.010856
8	8.0	6	0.000000	0.000022	0.0	6.0	0.000000	0.000028	0.000000	0.166667	0.549702	0.010856

	collections_12_mths_ex_med	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	0.0	269936	0.213421	0.984327	57610.0	212326.0	0.979729	0.985582	0.690173	NaN	NaN	0.001155
1	1.0	3999	0.278570	0.014582	1114.0	2885.0	0.018945	0.013392	0.881566	0.065149	0.191393	0.001155
2	2.0	250	0.268000	0.000912	67.0	183.0	0.001139	0.000849	0.850727	0.010570	0.030838	0.001155
3	3.0	29	0.241379	0.000106	7.0	22.0	0.000119	0.000102	0.772752	0.026621	0.077975	0.001155
4	4.0	11	0.363636	0.000040	4.0	7.0	0.000068	0.000032	1.129314	0.122257	0.356562	0.001155
5	5.0	3	0.000000	0.000011	0.0	3.0	0.000000	0.000014	0.000000	0.363636	1.129314	0.001155
6	6.0	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	0.000000	0.000000	0.001155
7	7.0	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	0.000000	0.000000	0.001155
8	9.0	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.000000	0.000000	0.001155
9	14.0	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.000000	0.000000	0.001155

	chargeoff_within_12_mths	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	0.0	271937	0.214256	0.991624	58264.0	213673.0	0.990851	0.991835	0.692651	NaN	NaN	inf
1	1.0	2080	0.236058	0.007585	491.0	1589.0	0.008350	0.007376	0.757096	0.021802	0.064445	inf
2	2.0	168	0.220238	0.000613	37.0	131.0	0.000629	0.000608	0.710388	0.015820	0.046708	inf
3	3.0	27	0.185185	0.000098	5.0	22.0	0.000085	0.000102	0.605766	0.035053	0.104622	inf
4	4.0	13	0.307692	0.000047	4.0	9.0	0.000068	0.000042	0.966339	0.122507	0.360573	inf
5	5.0	5	0.000000	0.000018	0.0	5.0	0.000000	0.000023	0.000000	0.307692	0.966339	inf
6	6.0	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.000000	0.000000	inf
7	7.0	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	0.000000	0.000000	inf
8	8.0	1	1.000000	0.000004	1.0	0.0	0.000017	0.000000	inf	1.000000	inf	inf

	total_acc_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(1.856, 4.483]	1324	0.270393	0.004828	358.0	966.0	0.006088	0.004484	0.857713	NaN	NaN	inf
1	(4.483, 6.966]	3984	0.231928	0.014528	924.0	3060.0	0.015714	0.014204	0.744928	0.038465	0.112786	inf
2	(6.966, 9.448]	12089	0.231367	0.044083	2797.0	9292.0	0.047566	0.043132	0.743275	0.000560	0.001652	inf
3	(9.448, 11.931]	12378	0.224592	0.045137	2780.0	9598.0	0.047277	0.044552	0.723270	0.006775	0.020005	inf
4	(11.931, 14.414]	23709	0.229449	0.086455	5440.0	18269.0	0.092514	0.084802	0.737615	0.004857	0.014345	inf
5	(14.414, 16.897]	17954	0.223293	0.065470	4009.0	13945.0	0.068178	0.064730	0.719429	0.006156	0.018187	inf
6	(16.897, 19.379]	29452	0.216148	0.107397	6366.0	23086.0	0.108262	0.107161	0.698267	0.007145	0.021161	inf
7	(19.379, 21.862]	19656	0.212810	0.071676	4183.0	15473.0	0.071137	0.071823	0.688359	0.003338	0.009908	inf
8	(21.862, 24.345]	28961	0.211629	0.105607	6129.0	22832.0	0.104231	0.105982	0.684851	0.001181	0.003509	inf
9	(24.345, 26.828]	17727	0.207875	0.064642	3685.0	14042.0	0.062668	0.065181	0.673684	0.003754	0.011167	inf
10	(26.828, 29.31]	23717	0.204748	0.086485	4856.0	18861.0	0.082582	0.087550	0.664368	0.003127	0.009316	inf
11	(29.31, 31.793]	13501	0.205614	0.049232	2776.0	10725.0	0.047209	0.049784	0.666951	0.000867	0.002583	inf
12	(31.793, 34.276]	17189	0.202106	0.062680	3474.0	13715.0	0.059080	0.063663	0.656488	0.003508	0.010463	inf
13	(34.276, 36.759]	9448	0.205758	0.034452	1944.0	7504.0	0.033060	0.034832	0.667378	0.003652	0.010891	inf
14	(36.759, 39.241]	11538	0.209828	0.042074	2421.0	9117.0	0.041172	0.042320	0.679496	0.004071	0.012118	inf
15	(39.241, 41.724]	5976	0.214692	0.021792	1283.0	4693.0	0.021819	0.021784	0.693947	0.004864	0.014450	inf
16	(41.724, 44.207]	7226	0.199972	0.026350	1445.0	5781.0	0.024574	0.026834	0.650116	0.014720	0.043831	inf
17	(44.207, 46.69]	3663	0.204750	0.013357	750.0	2913.0	0.012755	0.013522	0.664375	0.004778	0.014259	inf
18	(46.69, 49.172]	4301	0.214369	0.015684	922.0	3379.0	0.015680	0.015685	0.692987	0.009619	0.028612	inf
19	(49.172, 51.655]	2232	0.216846	0.008139	484.0	1748.0	0.008231	0.008114	0.700336	0.002477	0.007349	inf
20	(51.655, 54.138]	2389	0.226036	0.008712	540.0	1849.0	0.009183	0.008583	0.727538	0.009190	0.027202	inf
21	(54.138, 56.621]	1191	0.205709	0.004343	245.0	946.0	0.004167	0.004391	0.667234	0.020327	0.060304	inf
22	(56.621, 59.103]	1380	0.217391	0.005032	300.0	1080.0	0.005102	0.005013	0.701953	0.011682	0.034719	inf
23	(59.103, 61.586]	720	0.227778	0.002625	164.0	556.0	0.002789	0.002581	0.732683	0.010386	0.030729	inf
24	(61.586, 64.069]	1054	0.183112	0.003843	193.0	861.0	0.003282	0.003997	0.599520	0.044666	0.133163	inf
25	(64.069, 66.552]	288	0.232639	0.001050	67.0	221.0	0.001139	0.001026	0.747024	0.049527	0.147504	inf
26	(66.552, 69.034]	331	0.211480	0.001207	70.0	261.0	0.001190	0.001212	0.684408	0.021159	0.062616	inf
27	(69.034, 71.517]	167	0.209581	0.000609	35.0	132.0	0.000595	0.000613	0.678760	0.001900	0.005648	inf
28	(71.517, 74.0]	183	0.234973	0.000667	43.0	140.0	0.000731	0.000650	0.753901	0.025392	0.075141	inf
29	(74.0, 76.483]	98	0.204082	0.000357	20.0	78.0	0.000340	0.000362	0.662382	0.030891	0.091519	inf
30	(76.483, 78.966]	72	0.208333	0.000263	15.0	57.0	0.000255	0.000265	0.675048	0.004252	0.012666	inf
31	(78.966, 81.448]	86	0.232558	0.000314	20.0	66.0	0.000340	0.000306	0.746786	0.024225	0.071738	inf
32	(81.448, 83.931]	47	0.255319	0.000171	12.0	35.0	0.000204	0.000162	0.813647	0.022761	0.066860	inf
33	(83.931, 86.414]	59	0.203390	0.000215	12.0	47.0	0.000204	0.000218	0.660319	0.051929	0.153328	inf
34	(86.414, 88.897]	26	0.307692	0.000095	8.0	18.0	0.000136	0.000084	0.966339	0.104302	0.306020	inf
35	(88.897, 91.379]	34	0.294118	0.000124	10.0	24.0	0.000170	0.000111	0.926849	0.013575	0.039490	inf
36	(91.379, 93.862]	12	0.250000	0.000044	3.0	9.0	0.000051	0.000042	0.798060	0.044118	0.128789	inf
37	(93.862, 96.345]	26	0.346154	0.000095	9.0	17.0	0.000153	0.000079	1.078273	0.096154	0.280212	inf
38	(96.345, 98.828]	5	0.400000	0.000018	2.0	3.0	0.000034	0.000014	1.236185	0.053846	0.157913	inf
39	(98.828, 101.31]	9	0.222222	0.000033	2.0	7.0	0.000034	0.000032	0.716262	0.177778	0.519924	inf
40	(101.31, 103.793]	3	0.000000	0.000011	0.0	3.0	0.000000	0.000014	0.000000	0.222222	0.716262	inf
41	(103.793, 106.276]	7	0.142857	0.000026	1.0	6.0	0.000017	0.000028	0.476616	0.142857	0.476616	inf
42	(106.276, 108.759]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.142857	0.476616	inf
43	(108.759, 111.241]	6	0.333333	0.000022	2.0	4.0	0.000034	0.000019	1.040928	0.333333	1.040928	inf
44	(111.241, 113.724]	3	0.000000	0.000011	0.0	3.0	0.000000	0.000014	0.000000	0.333333	1.040928	inf
45	(113.724, 116.207]	4	0.250000	0.000015	1.0	3.0	0.000017	0.000014	0.798060	0.250000	0.798060	inf
46	(116.207, 118.69]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
47	(118.69, 121.172]	1	1.000000	0.000004	1.0	0.0	0.000017	0.000000	inf	NaN	NaN	inf
48	(121.172, 123.655]	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	1.000000	inf	inf
49	(123.655, 126.138]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.000000	0.000000	inf
50	(126.138, 128.621]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
51	(128.621, 131.103]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
52	(131.103, 133.586]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	inf
53	(133.586, 136.069]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
54	(136.069, 138.552]	1	1.000000	0.000004	1.0	0.0	0.000017	0.000000	inf	NaN	NaN	inf
55	(138.552, 141.034]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	1.000000	inf	inf
56	(141.034, 143.517]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
57	(143.517, 146.0]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	inf

	num_accts_ever_120_pd	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	0.0	212322	0.209159	0.774237	44409.0	167913.0	0.755229	0.779425	0.677504	NaN	NaN	inf
1	1.0	32932	0.236335	0.120087	7783.0	25149.0	0.132359	0.116738	0.757914	0.027177	0.080410	inf
2	2.0	13319	0.228771	0.048568	3047.0	10272.0	0.051818	0.047681	0.735615	0.007565	0.022299	inf
3	3.0	6079	0.230137	0.022167	1399.0	4680.0	0.023792	0.021724	0.739645	0.001366	0.004030	inf
4	4.0	3578	0.229737	0.013047	822.0	2756.0	0.013979	0.012793	0.738467	0.000399	0.001178	inf
5	5.0	2173	0.218132	0.007924	474.0	1699.0	0.008061	0.007886	0.704148	0.011606	0.034319	inf
6	6.0	1368	0.230994	0.004988	316.0	1052.0	0.005374	0.004883	0.742175	0.012863	0.038027	inf
7	7.0	830	0.231325	0.003027	192.0	638.0	0.003265	0.002961	0.743151	0.000331	0.000977	inf
8	8.0	501	0.195609	0.001827	98.0	403.0	0.001667	0.001871	0.637064	0.035717	0.106087	inf
9	9.0	351	0.225071	0.001280	79.0	272.0	0.001343	0.001263	0.724687	0.029462	0.087623	inf
10	10.0	261	0.206897	0.000952	54.0	207.0	0.000918	0.000961	0.670771	0.018175	0.053916	inf
11	11.0	140	0.221429	0.000511	31.0	109.0	0.000527	0.000506	0.713913	0.014532	0.043142	inf
12	12.0	109	0.266055	0.000397	29.0	80.0	0.000493	0.000371	0.845046	0.044626	0.131134	inf
13	13.0	67	0.268657	0.000244	18.0	49.0	0.000306	0.000227	0.852645	0.002602	0.007599	inf
14	14.0	67	0.164179	0.000244	11.0	56.0	0.000187	0.000260	0.542122	0.104478	0.310523	inf
15	15.0	28	0.285714	0.000102	8.0	20.0	0.000136	0.000093	0.902384	0.121535	0.360262	inf
16	16.0	28	0.178571	0.000102	5.0	23.0	0.000085	0.000107	0.585814	0.107143	0.316570	inf
17	17.0	17	0.411765	0.000062	7.0	10.0	0.000119	0.000046	1.271046	0.233193	0.685232	inf
18	18.0	15	0.333333	0.000055	5.0	10.0	0.000085	0.000046	1.040928	0.078431	0.230119	inf
19	19.0	10	0.300000	0.000036	3.0	7.0	0.000051	0.000032	0.943965	0.033333	0.096963	inf
20	20.0	7	0.428571	0.000026	3.0	4.0	0.000051	0.000019	1.321159	0.128571	0.377195	inf
21	21.0	6	0.333333	0.000022	2.0	4.0	0.000034	0.000019	1.040928	0.095238	0.280232	inf
22	22.0	6	0.333333	0.000022	2.0	4.0	0.000034	0.000019	1.040928	0.000000	0.000000	inf
23	23.0	2	0.500000	0.000007	1.0	1.0	0.000017	0.000005	1.539806	0.166667	0.498878	inf
24	24.0	4	0.000000	0.000015	0.0	4.0	0.000000	0.000019	0.000000	0.500000	1.539806	inf
25	25.0	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.000000	0.000000	inf
26	26.0	4	0.250000	0.000015	1.0	3.0	0.000017	0.000014	0.798060	0.250000	0.798060	inf
27	27.0	2	0.500000	0.000007	1.0	1.0	0.000017	0.000005	1.539806	0.250000	0.741746	inf
28	28.0	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	0.500000	1.539806	inf
29	29.0	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	0.000000	0.000000	inf
30	30.0	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.000000	0.000000	inf
31	34.0	2	1.000000	0.000007	2.0	0.0	0.000034	0.000000	inf	1.000000	inf	inf

	num_tl_90g_dpd_24m	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	0.0	259142	0.212849	0.944967	55158.0	203984.0	0.938029	0.946860	0.688473	NaN	NaN	inf
1	1.0	11266	0.242233	0.041082	2729.0	8537.0	0.046410	0.039627	0.775262	0.029385	0.086789	inf
2	2.0	2233	0.248992	0.008143	556.0	1677.0	0.009455	0.007784	0.795105	0.006759	0.019844	inf
3	3.0	623	0.240770	0.002272	150.0	473.0	0.002551	0.002196	0.770962	0.008222	0.024143	inf
4	4.0	349	0.217765	0.001273	76.0	273.0	0.001292	0.001267	0.703061	0.023005	0.067901	inf
5	5.0	199	0.190955	0.000726	38.0	161.0	0.000646	0.000747	0.623111	0.026810	0.079950	inf
6	6.0	147	0.170068	0.000536	25.0	122.0	0.000425	0.000566	0.560047	0.020887	0.063064	inf
7	7.0	71	0.281690	0.000259	20.0	51.0	0.000340	0.000237	0.890661	0.111622	0.330614	inf
8	8.0	45	0.266667	0.000164	12.0	33.0	0.000204	0.000153	0.846833	0.015023	0.043828	inf
9	9.0	53	0.169811	0.000193	9.0	44.0	0.000153	0.000204	0.559267	0.096855	0.287566	inf
10	10.0	29	0.241379	0.000106	7.0	22.0	0.000119	0.000102	0.772752	0.071568	0.213485	inf
11	11.0	15	0.333333	0.000055	5.0	10.0	0.000085	0.000046	1.040928	0.091954	0.268176	inf
12	12.0	17	0.176471	0.000062	3.0	14.0	0.000051	0.000065	0.579461	0.156863	0.461467	inf
13	13.0	14	0.214286	0.000051	3.0	11.0	0.000051	0.000051	0.692740	0.037815	0.113280	inf
14	14.0	12	0.500000	0.000044	6.0	6.0	0.000102	0.000028	1.539806	0.285714	0.847065	inf
15	15.0	5	0.200000	0.000018	1.0	4.0	0.000017	0.000019	0.650199	0.300000	0.889607	inf
16	16.0	3	0.333333	0.000011	1.0	2.0	0.000017	0.000009	1.040928	0.133333	0.390729	inf
17	17.0	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.333333	1.040928	inf
18	18.0	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	0.000000	0.000000	inf
19	20.0	3	0.333333	0.000011	1.0	2.0	0.000017	0.000009	1.040928	0.333333	1.040928	inf
20	22.0	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.333333	1.040928	inf
21	24.0	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.000000	0.000000	inf
22	26.0	2	0.500000	0.000007	1.0	1.0	0.000017	0.000005	1.539806	0.500000	1.539806	inf
23	36.0	1	1.000000	0.000004	1.0	0.0	0.000017	0.000000	inf	0.500000	inf	inf

Credit Risk Modeling¶

A project realised by Dr-eng.: Khalifa Mejbri, Expert in Engineering, Data Science and Machine Learning.¶

Part 1. Data Preprocessing and Feature Engineering¶

I. General Data Preparation¶

Import Libraries¶

Dataset Description¶

Explore Data¶

General Preprocessing¶

Preprocessing few continuous variables¶

Variable 'emp_length'¶

Fill with the mode (most frequent)¶

Convert string variable to date and time¶

Variable 'term'¶

Variable 'issue_d'¶

Check for missing values and clean¶

The following features with missing values > 90% can be dropped:¶

Considering the following three features:¶

Interpretation:¶

Fill Missing values of these 3 features:¶

Features with moderate missingness (around 38–67%)¶

Features with missing values ranging from ~1% to ~13%¶

Variable 'earliest_cr_line'¶

For the following features, the missing rates are all very low (under ~0.1% for most), so we can confidently impute them using simple and fast methods without significant risk of bias or information loss. Here's a breakdown with recommended strategies:¶

Variables 'last_pymnt_d' & 'last_credit_pull_d'¶

For the following features, the missing rate is extremely low (~0.0015%), so imputation is safe and won't significantly affect our model. Here’s a breakdown and recommended strategies:¶

loan_status (0.0015% missing)¶

Verify if there are remaining missing values¶

The final version of the dataset is composed of 2260668 rows and 118 columns.¶

II. PD model (Probability of Default)¶

Data preparation¶

Dependent Variable — key target variable for credit risk modeling¶

Typical Modeling Strategy: Grouping Loan Status¶

For the two statuses:¶

Splitting Data¶

Save inputs data & targets data for training & testing¶

A. Preprocessing Discrete Variables¶

What is Weight of Evidence (WoE)?¶

How to Use WoE with Discrete (Categorical) Variables?¶

Why Use WoE?¶

Bonus: Use WoE Together with Information Value (IV)¶

Preprocessing Discrete Variables: Visualizing Results¶

List of the categorical variables in the dataset¶

Certain categorical variables should be dropped from the training of the credit risk model:¶

Variable 'grade'¶

Variable 'home_ownership'¶

Variable 'addr_state'¶

Variable 'verification_status'¶

Variable 'purpose'¶

Variables: 'initial_list_status', 'application_type', 'hardship_flag', 'disbursement_method', 'debt_settlement_flag'¶

B. Preprocessing Continuous Variables¶

Check the list of numerical features¶

Check for multicollinearity, the most common approach is to calculate the correlation matrix and/or compute the Variance Inflation Factor (VIF) for each feature.¶

Function that calculates the Variance Inflation Factor (VIF) for a given list of numerical features:¶

Function version of the highly correlated feature removal process:¶

1. Loan Information¶

Recommendations¶

VIF analysis of the new set of features.¶

2. Applicant Income & Employment¶

Recommendation¶

3. Credit History and Delinquency¶

Recommendations¶

VIF analysis of the new set of features.¶

4. Credit Utilization & Balance¶

Recommendations:¶

VIF analysis of the new set of features.¶

5. Credit Limits¶

Recommendations¶

VIF analysis of the new set of features.¶

6. Account and Credit Line Counts¶

1. Recommended Features to KEEP (low redundancy, strong representation):¶

2. Recommended Features to DROP (high VIF or redundant info):¶

VIF analysis of the new set of features.¶

7. Credit Inquiries¶

1. Keep All Features¶

8. Payment and Recovery¶

1. Drop Highly Redundant Features¶

2. Create Aggregated Features (Optional)¶

VIF analysis of the new set of features.¶

9. FICO Scores¶

1. Drop One of Each Pair¶

	revol_bal_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(-2904.836, 58096.72]	267276	0.215605	0.974628	57626.0	209650.0	0.980001	0.973161	0.696655	NaN	NaN	0.001207
1	(58096.72, 116193.44]	5128	0.177457	0.018699	910.0	4218.0	0.015476	0.019579	0.582445	0.038148	0.114210	0.001207
2	(116193.44, 174290.16]	1030	0.151456	0.003756	156.0	874.0	0.002653	0.004057	0.503154	0.026001	0.079291	0.001207
3	(174290.16, 232386.88]	402	0.131841	0.001466	53.0	349.0	0.000901	0.001620	0.442360	0.019616	0.060794	0.001207
4	(232386.88, 290483.6]	198	0.111111	0.000722	22.0	176.0	0.000374	0.000817	0.377039	0.020730	0.065322	0.001207
5	(290483.6, 348580.32]	86	0.139535	0.000314	12.0	74.0	0.000204	0.000343	0.466316	0.028424	0.089278	0.001207
6	(348580.32, 406677.04]	45	0.177778	0.000164	8.0	37.0	0.000136	0.000172	0.583415	0.038243	0.117099	0.001207
7	(406677.04, 464773.76]	29	0.275862	0.000106	8.0	21.0	0.000136	0.000097	0.873671	0.098084	0.290256	0.001207
8	(464773.76, 522870.48]	10	0.300000	0.000036	3.0	7.0	0.000051	0.000032	0.943965	0.024138	0.070293	0.001207
9	(522870.48, 580967.2]	11	0.181818	0.000040	2.0	9.0	0.000034	0.000042	0.595618	0.118182	0.348346	0.001207
10	(580967.2, 639063.92]	6	0.000000	0.000022	0.0	6.0	0.000000	0.000028	0.000000	0.181818	0.595618	0.001207
11	(639063.92, 697160.64]	3	0.000000	0.000011	0.0	3.0	0.000000	0.000014	0.000000	0.000000	0.000000	0.001207
12	(697160.64, 755257.36]	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	0.000000	0.000000	0.001207
13	(755257.36, 813354.08]	3	0.666667	0.000011	2.0	1.0	0.000034	0.000005	2.119548	0.666667	2.119548	0.001207
14	(813354.08, 871450.8]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.666667	2.119548	0.001207
15	(871450.8, 929547.52]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.000000	0.000000	0.001207
16	(929547.52, 987644.24]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
17	(987644.24, 1045740.96]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
18	(1045740.96, 1103837.68]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
19	(1103837.68, 1161934.4]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
20	(1161934.4, 1220031.12]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
21	(1220031.12, 1278127.84]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
22	(1278127.84, 1336224.56]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
23	(1336224.56, 1394321.28]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
24	(1394321.28, 1452418.0]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
25	(1452418.0, 1510514.72]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
26	(1510514.72, 1568611.44]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
27	(1568611.44, 1626708.16]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
28	(1626708.16, 1684804.88]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
29	(1684804.88, 1742901.6]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
30	(1742901.6, 1800998.32]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
31	(1800998.32, 1859095.04]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
32	(1859095.04, 1917191.76]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
33	(1917191.76, 1975288.48]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
34	(1975288.48, 2033385.2]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
35	(2033385.2, 2091481.92]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
36	(2091481.92, 2149578.64]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
37	(2149578.64, 2207675.36]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
38	(2207675.36, 2265772.08]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
39	(2265772.08, 2323868.8]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
40	(2323868.8, 2381965.52]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
41	(2381965.52, 2440062.24]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
42	(2440062.24, 2498158.96]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
43	(2498158.96, 2556255.68]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
44	(2556255.68, 2614352.4]	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	NaN	NaN	0.001207
45	(2614352.4, 2672449.12]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
46	(2672449.12, 2730545.84]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
47	(2730545.84, 2788642.56]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
48	(2788642.56, 2846739.28]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.001207
49	(2846739.28, 2904836.0]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	0.001207

	revol_bal_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(-99.967, 1999.34]	16936	0.217820	0.062315	3689.0	13247.0	0.063122	0.062094	0.701395	NaN	NaN	0.001701
1	(1999.34, 3998.68]	23741	0.212839	0.087354	5053.0	18688.0	0.086462	0.087598	0.686640	0.004981	0.014754	0.001701
2	(3998.68, 5998.02]	28965	0.211289	0.106575	6120.0	22845.0	0.104719	0.107084	0.682046	0.001549	0.004594	0.001701
3	(5998.02, 7997.36]	28485	0.215025	0.104809	6125.0	22360.0	0.104805	0.104810	0.693121	0.003736	0.011075	0.001701
4	(7997.36, 9996.7]	25966	0.219749	0.095541	5706.0	20260.0	0.097635	0.094967	0.707100	0.004723	0.013978	0.001701
5	(9996.7, 11996.04]	22836	0.222762	0.084024	5087.0	17749.0	0.087044	0.083197	0.716004	0.003013	0.008904	0.001701
6	(11996.04, 13995.38]	19075	0.220708	0.070185	4210.0	14865.0	0.072037	0.069678	0.709934	0.002055	0.006070	0.001701
7	(13995.38, 15994.72]	16227	0.225057	0.059706	3652.0	12575.0	0.062489	0.058944	0.722777	0.004349	0.012843	0.001701
8	(15994.72, 17994.06]	13461	0.224203	0.049529	3018.0	10443.0	0.051641	0.048950	0.720258	0.000854	0.002519	0.001701
9	(17994.06, 19993.4]	11389	0.219773	0.041905	2503.0	8886.0	0.042829	0.041652	0.707172	0.004430	0.013086	0.001701
10	(19993.4, 21992.74]	9359	0.213698	0.034436	2000.0	7359.0	0.034222	0.034495	0.689188	0.006075	0.017984	0.001701
11	(21992.74, 23992.08]	7979	0.212683	0.029358	1697.0	6282.0	0.029037	0.029446	0.686180	0.001015	0.003008	0.001701
12	(23992.08, 25991.42]	6614	0.217569	0.024336	1439.0	5175.0	0.024623	0.024257	0.700651	0.004885	0.014471	0.001701
13	(25991.42, 27990.76]	5734	0.206836	0.021098	1186.0	4548.0	0.020294	0.021318	0.668821	0.010732	0.031830	0.001701
14	(27990.76, 29990.1]	4853	0.208325	0.017856	1011.0	3842.0	0.017299	0.018009	0.673244	0.001488	0.004423	0.001701
15	(29990.1, 31989.44]	4215	0.207117	0.015509	873.0	3342.0	0.014938	0.015665	0.669657	0.001207	0.003588	0.001701
16	(31989.44, 33988.78]	3551	0.193467	0.013066	687.0	2864.0	0.011755	0.013425	0.628951	0.013651	0.040705	0.001701
17	(33988.78, 35988.12]	3121	0.210189	0.011484	656.0	2465.0	0.011225	0.011554	0.678780	0.016722	0.049829	0.001701
18	(35988.12, 37987.46]	2619	0.195494	0.009636	512.0	2107.0	0.008761	0.009876	0.635015	0.014695	0.043765	0.001701
19	(37987.46, 39986.8]	2186	0.208600	0.008043	456.0	1730.0	0.007803	0.008109	0.674062	0.013106	0.039047	0.001701
20	(39986.8, 41986.14]	1907	0.202412	0.007017	386.0	1521.0	0.006605	0.007130	0.655656	0.006188	0.018406	0.001701
21	(41986.14, 43985.48]	1626	0.178352	0.005983	290.0	1336.0	0.004962	0.006262	0.583546	0.024060	0.072110	0.001701
22	(43985.48, 45984.82]	1421	0.192118	0.005228	273.0	1148.0	0.004671	0.005381	0.624916	0.013766	0.041370	0.001701
23	(45984.82, 47984.16]	1257	0.171838	0.004625	216.0	1041.0	0.003696	0.004880	0.563856	0.020281	0.061059	0.001701
24	(47984.16, 49983.5]	1083	0.228994	0.003985	248.0	835.0	0.004244	0.003914	0.734384	0.057156	0.170528	0.001701
25	(49983.5, 51982.84]	815	0.218405	0.002999	178.0	637.0	0.003046	0.002986	0.703125	0.010589	0.031259	0.001701
26	(51982.84, 53982.18]	698	0.206304	0.002568	144.0	554.0	0.002464	0.002597	0.667238	0.012101	0.035887	0.001701
27	(53982.18, 55981.52]	608	0.177632	0.002237	108.0	500.0	0.001848	0.002344	0.581372	0.028672	0.085865	0.001701
28	(55981.52, 57980.86]	515	0.182524	0.001895	94.0	421.0	0.001608	0.001973	0.596118	0.004893	0.014745	0.001701
29	(57980.86, 59980.2]	455	0.184615	0.001674	84.0	371.0	0.001437	0.001739	0.602407	0.002091	0.006290	0.001701
30	(59980.2, 61979.54]	394	0.190355	0.001450	75.0	319.0	0.001283	0.001495	0.619635	0.005740	0.017228	0.001701
31	(61979.54, 63978.88]	379	0.195251	0.001395	74.0	305.0	0.001266	0.001430	0.634287	0.004895	0.014651	0.001701
32	(63978.88, 65978.22]	351	0.170940	0.001291	60.0	291.0	0.001027	0.001364	0.561137	0.024310	0.073149	0.001701
33	(65978.22, 67977.56]	296	0.199324	0.001089	59.0	237.0	0.001010	0.001111	0.646451	0.028384	0.085314	0.001701
34	(67977.56, 69976.9]	269	0.226766	0.000990	61.0	208.0	0.001044	0.000975	0.727817	0.027441	0.081366	0.001701
35	(69976.9, 71976.24]	261	0.176245	0.000960	46.0	215.0	0.000787	0.001008	0.577187	0.050521	0.150631	0.001701
36	(71976.24, 73975.58]	185	0.140541	0.000681	26.0	159.0	0.000445	0.000745	0.468080	0.035705	0.109107	0.001701
37	(73975.58, 75974.92]	251	0.187251	0.000924	47.0	204.0	0.000804	0.000956	0.610325	0.046710	0.142245	0.001701
38	(75974.92, 77974.26]	187	0.219251	0.000688	41.0	146.0	0.000702	0.000684	0.705628	0.032000	0.095304	0.001701
39	(77974.26, 79973.6]	187	0.155080	0.000688	29.0	158.0	0.000496	0.000741	0.512832	0.064171	0.192796	0.001701
40	(79973.6, 81972.94]	157	0.184713	0.000578	29.0	128.0	0.000496	0.000600	0.602702	0.029633	0.089870	0.001701
41	(81972.94, 83972.28]	183	0.213115	0.000673	39.0	144.0	0.000667	0.000675	0.687459	0.028401	0.084757	0.001701
42	(83972.28, 85971.62]	131	0.091603	0.000482	12.0	119.0	0.000205	0.000558	0.313430	0.121512	0.374029	0.001701
43	(85971.62, 87970.96]	172	0.174419	0.000633	30.0	142.0	0.000513	0.000666	0.571666	0.082816	0.258236	0.001701
44	(87970.96, 89970.3]	112	0.178571	0.000412	20.0	92.0	0.000342	0.000431	0.584208	0.004153	0.012542	0.001701
45	(89970.3, 91969.64]	131	0.213740	0.000482	28.0	103.0	0.000479	0.000483	0.689314	0.035169	0.105106	0.001701
46	(91969.64, 93968.98]	116	0.155172	0.000427	18.0	98.0	0.000308	0.000459	0.513114	0.058568	0.176199	0.001701
47	(93968.98, 95968.32]	105	0.171429	0.000386	18.0	87.0	0.000308	0.000408	0.562617	0.016256	0.049502	0.001701
48	(95968.32, 97967.66]	118	0.169492	0.000434	20.0	98.0	0.000342	0.000459	0.556746	0.001937	0.005871	0.001701
49	(97967.66, 99967.0]	98	0.091837	0.000361	9.0	89.0	0.000154	0.000417	0.314186	0.077655	0.242560	0.001701

	total_bal_il_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(-1044.916, 20898.32]	212152	0.202082	0.773617	42872.0	169280.0	0.729091	0.785770	0.656415	NaN	NaN	0.009611
1	(20898.32, 41796.64]	29386	0.261077	0.107157	7672.0	21714.0	0.130472	0.100793	0.830495	0.058995	0.174080	0.009611
2	(41796.64, 62694.96]	14531	0.259032	0.052988	3764.0	10767.0	0.064011	0.049979	0.824516	0.002044	0.005980	0.009611
3	(62694.96, 83593.28]	7465	0.255325	0.027221	1906.0	5559.0	0.032414	0.025804	0.813663	0.003708	0.010852	0.009611
4	(83593.28, 104491.6]	4013	0.238973	0.014633	959.0	3054.0	0.016309	0.014176	0.765677	0.016352	0.047986	0.009611
5	(104491.6, 125389.92]	2247	0.252781	0.008194	568.0	1679.0	0.009660	0.007794	0.806213	0.013808	0.040536	0.009611
6	(125389.92, 146288.24]	1410	0.248936	0.005142	351.0	1059.0	0.005969	0.004916	0.794940	0.003845	0.011273	0.009611
7	(146288.24, 167186.56]	930	0.252688	0.003391	235.0	695.0	0.003996	0.003226	0.805940	0.003752	0.011000	0.009611
8	(167186.56, 188084.88]	612	0.215686	0.002232	132.0	480.0	0.002245	0.002228	0.696897	0.037002	0.109043	0.009611
9	(188084.88, 208983.2]	411	0.243309	0.001499	100.0	311.0	0.001701	0.001444	0.778423	0.027623	0.081526	0.009611
10	(208983.2, 229881.52]	276	0.260870	0.001006	72.0	204.0	0.001224	0.000947	0.829889	0.017561	0.051467	0.009611
11	(229881.52, 250779.84]	179	0.268156	0.000653	48.0	131.0	0.000816	0.000608	0.851184	0.007287	0.021295	0.009611
12	(250779.84, 271678.16]	160	0.231250	0.000583	37.0	123.0	0.000629	0.000571	0.742929	0.036906	0.108255	0.009611
13	(271678.16, 292576.48]	100	0.180000	0.000365	18.0	82.0	0.000306	0.000381	0.590130	0.051250	0.152799	0.009611
14	(292576.48, 313474.8]	87	0.160920	0.000317	14.0	73.0	0.000238	0.000339	0.532171	0.019080	0.057959	0.009611
15	(313474.8, 334373.12]	54	0.129630	0.000197	7.0	47.0	0.000119	0.000218	0.435448	0.031290	0.096723	0.009611
16	(334373.12, 355271.44]	48	0.229167	0.000175	11.0	37.0	0.000187	0.000172	0.736783	0.099537	0.301335	0.009611
17	(355271.44, 376169.76]	33	0.212121	0.000120	7.0	26.0	0.000119	0.000121	0.686312	0.017045	0.050471	0.009611
18	(376169.76, 397068.08]	29	0.206897	0.000106	6.0	23.0	0.000102	0.000107	0.670771	0.005225	0.015542	0.009611
19	(397068.08, 417966.4]	15	0.200000	0.000055	3.0	12.0	0.000051	0.000056	0.650199	0.006897	0.020572	0.009611
20	(417966.4, 438864.72]	25	0.160000	0.000091	4.0	21.0	0.000068	0.000097	0.529360	0.040000	0.120839	0.009611
21	(438864.72, 459763.04]	11	0.272727	0.000040	3.0	8.0	0.000051	0.000037	0.864527	0.112727	0.335167	0.009611
22	(459763.04, 480661.36]	8	0.375000	0.000029	3.0	5.0	0.000051	0.000023	1.162592	0.102273	0.298065	0.009611
23	(480661.36, 501559.68]	10	0.100000	0.000036	1.0	9.0	0.000017	0.000042	0.341514	0.275000	0.821078	0.009611
24	(501559.68, 522458.0]	13	0.307692	0.000047	4.0	9.0	0.000068	0.000042	0.966339	0.207692	0.624825	0.009611
25	(522458.0, 543356.32]	7	0.428571	0.000026	3.0	4.0	0.000051	0.000019	1.321159	0.120879	0.354820	0.009611
26	(543356.32, 564254.64]	7	0.285714	0.000026	2.0	5.0	0.000034	0.000023	0.902384	0.142857	0.418775	0.009611
27	(564254.64, 585152.96]	3	0.000000	0.000011	0.0	3.0	0.000000	0.000014	0.000000	0.285714	0.902384	0.009611
28	(585152.96, 606051.28]	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	0.000000	0.000000	0.009611
29	(606051.28, 626949.6]	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	0.000000	0.000000	0.009611
30	(626949.6, 647847.92]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.009611
31	(647847.92, 668746.24]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.009611
32	(668746.24, 689644.56]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.009611
33	(689644.56, 710542.88]	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	NaN	NaN	0.009611
34	(710542.88, 731441.2]	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	0.000000	0.000000	0.009611
35	(731441.2, 752339.52]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.009611
36	(752339.52, 773237.84]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.009611
37	(773237.84, 794136.16]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	0.009611
38	(794136.16, 815034.48]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.009611
39	(815034.48, 835932.8]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.009611
40	(835932.8, 856831.12]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.009611
41	(856831.12, 877729.44]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.009611
42	(877729.44, 898627.76]	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	NaN	NaN	0.009611
43	(898627.76, 919526.08]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.009611
44	(919526.08, 940424.4]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.009611
45	(940424.4, 961322.72]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.009611
46	(961322.72, 982221.04]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.009611
47	(982221.04, 1003119.36]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.009611
48	(1003119.36, 1024017.68]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.009611
49	(1024017.68, 1044916.0]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	0.009611

	total_bal_il_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(-198.948, 3999.96]	5316	0.245109	0.053300	1303.0	4013.0	0.050998	0.054093	0.664122	NaN	NaN	0.001671
1	(3999.96, 7998.92]	7607	0.245958	0.076271	1871.0	5736.0	0.073229	0.077318	0.666347	0.000849	0.002226	0.001671
2	(7998.92, 11997.88]	7994	0.260195	0.080151	2080.0	5914.0	0.081409	0.079717	0.703701	0.014237	0.037353	0.001671
3	(11997.88, 15996.84]	8308	0.258305	0.083299	2146.0	6162.0	0.083992	0.083060	0.698741	0.001890	0.004960	0.001671
4	(15996.84, 19995.8]	7995	0.259162	0.080161	2072.0	5923.0	0.081096	0.079839	0.700989	0.000857	0.002248	0.001671
5	(19995.8, 23994.76]	7196	0.259728	0.072150	1869.0	5327.0	0.073151	0.071805	0.702474	0.000566	0.001485	0.001671
6	(23994.76, 27993.72]	6462	0.250851	0.064790	1621.0	4841.0	0.063444	0.065254	0.679183	0.008876	0.023291	0.001671
7	(27993.72, 31992.68]	5914	0.263274	0.059296	1557.0	4357.0	0.060939	0.058730	0.711782	0.012422	0.032599	0.001671
8	(31992.68, 35991.64]	5061	0.265165	0.050743	1342.0	3719.0	0.052524	0.050130	0.716748	0.001891	0.004966	0.001671
9	(35991.64, 39990.6]	4527	0.261763	0.045389	1185.0	3342.0	0.046380	0.045048	0.707816	0.003402	0.008933	0.001671
10	(39990.6, 43989.56]	3971	0.270964	0.039815	1076.0	2895.0	0.042114	0.039023	0.731982	0.009202	0.024166	0.001671
11	(43989.56, 47988.52]	3394	0.266352	0.034029	904.0	2490.0	0.035382	0.033564	0.719866	0.004612	0.012115	0.001671
12	(47988.52, 51987.48]	2894	0.252937	0.029016	732.0	2162.0	0.028650	0.029143	0.684655	0.013415	0.035211	0.001671
13	(51987.48, 55986.44]	2548	0.263736	0.025547	672.0	1876.0	0.026301	0.025287	0.712997	0.010799	0.028342	0.001671
14	(55986.44, 59985.4]	2207	0.241504	0.022128	533.0	1674.0	0.020861	0.022565	0.654668	0.022232	0.058329	0.001671
15	(59985.4, 63984.36]	1995	0.260652	0.020003	520.0	1475.0	0.020352	0.019882	0.704899	0.019147	0.050231	0.001671
16	(63984.36, 67983.32]	1719	0.253054	0.017235	435.0	1284.0	0.017025	0.017308	0.684962	0.007598	0.019937	0.001671
17	(67983.32, 71982.28]	1582	0.256005	0.015862	405.0	1177.0	0.015851	0.015865	0.692705	0.002951	0.007743	0.001671
18	(71982.28, 75981.24]	1384	0.256503	0.013876	355.0	1029.0	0.013894	0.013870	0.694011	0.000498	0.001306	0.001671
19	(75981.24, 79980.2]	1206	0.252073	0.012092	304.0	902.0	0.011898	0.012158	0.682388	0.004430	0.011623	0.001671
20	(79980.2, 83979.16]	1094	0.269653	0.010969	295.0	799.0	0.011546	0.010770	0.728535	0.017580	0.046147	0.001671
21	(83979.16, 87978.12]	972	0.215021	0.009746	209.0	763.0	0.008180	0.010285	0.585200	0.054632	0.143335	0.001671
22	(87978.12, 91977.08]	857	0.252042	0.008593	216.0	641.0	0.008454	0.008640	0.682307	0.037021	0.097107	0.001671
23	(91977.08, 95976.04]	764	0.246073	0.007660	188.0	576.0	0.007358	0.007764	0.666651	0.005969	0.015656	0.001671
24	(95976.04, 99975.0]	661	0.210287	0.006627	139.0	522.0	0.005440	0.007036	0.572775	0.035786	0.093876	0.001671
25	(99975.0, 103973.96]	595	0.262185	0.005966	156.0	439.0	0.006106	0.005917	0.708924	0.051897	0.136149	0.001671
26	(103973.96, 107972.92]	507	0.230769	0.005083	117.0	390.0	0.004579	0.005257	0.626516	0.031416	0.082408	0.001671
27	(107972.92, 111971.88]	462	0.259740	0.004632	120.0	342.0	0.004697	0.004610	0.702507	0.028971	0.075991	0.001671
28	(111971.88, 115970.84]	460	0.252174	0.004612	116.0	344.0	0.004540	0.004637	0.682653	0.007566	0.019854	0.001671
29	(115970.84, 119969.8]	388	0.280928	0.003890	109.0	279.0	0.004266	0.003761	0.758177	0.028754	0.075524	0.001671
30	(119969.8, 123968.76]	363	0.250689	0.003640	91.0	272.0	0.003562	0.003666	0.678757	0.030239	0.079420	0.001671
31	(123968.76, 127967.72]	324	0.231481	0.003249	75.0	249.0	0.002935	0.003356	0.628384	0.019207	0.050373	0.001671
32	(127967.72, 131966.68]	320	0.250000	0.003208	80.0	240.0	0.003131	0.003235	0.676950	0.018519	0.048566	0.001671
33	(131966.68, 135965.64]	293	0.242321	0.002938	71.0	222.0	0.002779	0.002992	0.656809	0.007679	0.020141	0.001671
34	(135965.64, 139964.6]	241	0.286307	0.002416	69.0	172.0	0.002701	0.002318	0.772336	0.043986	0.115526	0.001671
35	(139964.6, 143963.56]	237	0.253165	0.002376	60.0	177.0	0.002348	0.002386	0.685252	0.033142	0.087084	0.001671
36	(143963.56, 147962.52]	217	0.253456	0.002176	55.0	162.0	0.002153	0.002184	0.686017	0.000292	0.000765	0.001671
37	(147962.52, 151961.48]	179	0.256983	0.001795	46.0	133.0	0.001800	0.001793	0.695271	0.003527	0.009254	0.001671
38	(151961.48, 155960.44]	189	0.243386	0.001895	46.0	143.0	0.001800	0.001928	0.659604	0.013597	0.035668	0.001671
39	(155960.44, 159959.4]	184	0.217391	0.001845	40.0	144.0	0.001566	0.001941	0.591422	0.025995	0.068181	0.001671
40	(159959.4, 163958.36]	169	0.254438	0.001694	43.0	126.0	0.001683	0.001698	0.688593	0.037047	0.097170	0.001671
41	(163958.36, 167957.32]	139	0.287770	0.001394	40.0	99.0	0.001566	0.001334	0.776188	0.033332	0.087595	0.001671
42	(167957.32, 171956.28]	128	0.195312	0.001283	25.0	103.0	0.000978	0.001388	0.533423	0.092457	0.242765	0.001671
43	(171956.28, 175955.24]	114	0.175439	0.001143	20.0	94.0	0.000783	0.001267	0.481059	0.019874	0.052363	0.001671
44	(175955.24, 179954.2]	124	0.217742	0.001243	27.0	97.0	0.001057	0.001308	0.592342	0.042303	0.111283	0.001671
45	(179954.2, 183953.16]	107	0.280374	0.001073	30.0	77.0	0.001174	0.001038	0.756719	0.062632	0.164377	0.001671
46	(183953.16, 187952.12]	110	0.200000	0.001103	22.0	88.0	0.000861	0.001186	0.545749	0.080374	0.210971	0.001671
47	(187952.12, 191951.08]	108	0.194444	0.001083	21.0	87.0	0.000822	0.001173	0.531139	0.005556	0.014609	0.001671
48	(191951.08, 195950.04]	77	0.311688	0.000772	24.0	53.0	0.000939	0.000714	0.839340	0.117244	0.308200	0.001671
49	(195950.04, 199949.0]	74	0.243243	0.000742	18.0	56.0	0.000705	0.000755	0.659229	0.068445	0.180111	0.001671

	max_bal_bc_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(-48.997, 1000.94]	9830	0.278535	0.089429	2738.0	7092.0	0.097109	0.086780	0.750959	NaN	NaN	0.0056
1	(1000.94, 2000.88]	13143	0.278095	0.119570	3655.0	9488.0	0.129633	0.116098	0.749802	0.000440	0.001157	0.0056
2	(2000.88, 3000.82]	14661	0.267103	0.133380	3916.0	10745.0	0.138890	0.131479	0.720940	0.010992	0.028862	0.0056
3	(3000.82, 4000.76]	13546	0.262734	0.123236	3559.0	9987.0	0.126228	0.122204	0.709478	0.004369	0.011462	0.0056
4	(4000.76, 5000.7]	12530	0.259936	0.113993	3257.0	9273.0	0.115517	0.113467	0.702139	0.002798	0.007339	0.0056
5	(5000.7, 6000.64]	9542	0.262104	0.086809	2501.0	7041.0	0.088704	0.086156	0.707825	0.002168	0.005687	0.0056
6	(6000.64, 7000.58]	7337	0.243287	0.066749	1785.0	5552.0	0.063309	0.067936	0.658501	0.018817	0.049324	0.0056
7	(7000.58, 8000.52]	5866	0.247017	0.053367	1449.0	4417.0	0.051392	0.054048	0.668272	0.003729	0.009772	0.0056
8	(8000.52, 9000.46]	4461	0.239632	0.040584	1069.0	3392.0	0.037915	0.041506	0.648924	0.007384	0.019349	0.0056
9	(9000.46, 10000.4]	3969	0.244646	0.036108	971.0	2998.0	0.034439	0.036684	0.662060	0.005014	0.013136	0.0056
10	(10000.4, 11000.34]	2489	0.233427	0.022644	581.0	1908.0	0.020606	0.023347	0.632666	0.011219	0.029394	0.0056
11	(11000.34, 12000.28]	2023	0.235788	0.018404	477.0	1546.0	0.016918	0.018917	0.638853	0.002361	0.006187	0.0056
12	(12000.28, 13000.22]	1607	0.212819	0.014620	342.0	1265.0	0.012130	0.015479	0.578653	0.022970	0.060200	0.0056
13	(13000.22, 14000.16]	1419	0.208598	0.012910	296.0	1123.0	0.010498	0.013741	0.567580	0.004221	0.011073	0.0056
14	(14000.16, 15000.1]	1316	0.219605	0.011972	289.0	1027.0	0.010250	0.012567	0.596445	0.011007	0.028865	0.0056
15	(15000.1, 16000.04]	1047	0.225406	0.009525	236.0	811.0	0.008370	0.009924	0.611649	0.005801	0.015204	0.0056
16	(16000.04, 16999.98]	789	0.212928	0.007178	168.0	621.0	0.005959	0.007599	0.578938	0.012478	0.032711	0.0056
17	(16999.98, 17999.92]	775	0.216774	0.007051	168.0	607.0	0.005959	0.007427	0.589024	0.003846	0.010086	0.0056
18	(17999.92, 18999.86]	602	0.215947	0.005477	130.0	472.0	0.004611	0.005776	0.586855	0.000827	0.002169	0.0056
19	(18999.86, 19999.8]	591	0.204738	0.005377	121.0	470.0	0.004292	0.005751	0.557452	0.011209	0.029403	0.0056
20	(19999.8, 20999.74]	398	0.233668	0.003621	93.0	305.0	0.003298	0.003732	0.633298	0.028931	0.075847	0.0056
21	(20999.74, 21999.68]	298	0.167785	0.002711	50.0	248.0	0.001773	0.003035	0.460194	0.065883	0.173105	0.0056
22	(21999.68, 22999.62]	269	0.189591	0.002447	51.0	218.0	0.001809	0.002668	0.517660	0.021806	0.057466	0.0056
23	(22999.62, 23999.56]	268	0.205224	0.002438	55.0	213.0	0.001951	0.002606	0.558728	0.015633	0.041068	0.0056
24	(23999.56, 24999.5]	261	0.203065	0.002374	53.0	208.0	0.001880	0.002545	0.553061	0.002159	0.005666	0.0056
25	(24999.5, 25999.44]	147	0.210884	0.001337	31.0	116.0	0.001099	0.001419	0.573579	0.007819	0.020517	0.0056
26	(25999.44, 26999.38]	116	0.215517	0.001055	25.0	91.0	0.000887	0.001114	0.585728	0.004633	0.012150	0.0056
27	(26999.38, 27999.32]	94	0.138298	0.000855	13.0	81.0	0.000461	0.000991	0.381989	0.077219	0.203739	0.0056
28	(27999.32, 28999.26]	70	0.171429	0.000637	12.0	58.0	0.000426	0.000710	0.469813	0.033131	0.087824	0.0056
29	(28999.26, 29999.2]	78	0.282051	0.000710	22.0	56.0	0.000780	0.000685	0.760202	0.110623	0.290388	0.0056
30	(29999.2, 30999.14]	52	0.153846	0.000473	8.0	44.0	0.000284	0.000538	0.423308	0.128205	0.336893	0.0056
31	(30999.14, 31999.08]	37	0.216216	0.000337	8.0	29.0	0.000284	0.000355	0.587561	0.062370	0.164253	0.0056
32	(31999.08, 32999.02]	26	0.115385	0.000237	3.0	23.0	0.000106	0.000281	0.320683	0.100832	0.266878	0.0056
33	(32999.02, 33998.96]	30	0.366667	0.000273	11.0	19.0	0.000390	0.000232	0.985106	0.251282	0.664423	0.0056
34	(33998.96, 34998.9]	49	0.244898	0.000446	12.0	37.0	0.000426	0.000453	0.662721	0.121769	0.322385	0.0056
35	(34998.9, 35998.84]	24	0.166667	0.000218	4.0	20.0	0.000142	0.000245	0.457239	0.078231	0.205482	0.0056
36	(35998.84, 36998.78]	13	0.307692	0.000118	4.0	9.0	0.000142	0.000110	0.827781	0.141026	0.370542	0.0056
37	(36998.78, 37998.72]	27	0.222222	0.000246	6.0	21.0	0.000213	0.000257	0.603305	0.085470	0.224476	0.0056
38	(37998.72, 38998.66]	17	0.294118	0.000155	5.0	12.0	0.000177	0.000147	0.791960	0.071895	0.188655	0.0056
39	(38998.66, 39998.6]	15	0.133333	0.000136	2.0	13.0	0.000071	0.000159	0.368751	0.160784	0.423209	0.0056
40	(39998.6, 40998.54]	13	0.153846	0.000118	2.0	11.0	0.000071	0.000135	0.423308	0.020513	0.054557	0.0056
41	(40998.54, 41998.48]	3	0.000000	0.000027	0.0	3.0	0.000000	0.000037	0.000000	0.153846	0.423308	0.0056
42	(41998.48, 42998.42]	11	0.272727	0.000100	3.0	8.0	0.000106	0.000098	0.735703	0.272727	0.735703	0.0056
43	(42998.42, 43998.36]	9	0.000000	0.000082	0.0	9.0	0.000000	0.000110	0.000000	0.272727	0.735703	0.0056
44	(43998.36, 44998.3]	8	0.375000	0.000073	3.0	5.0	0.000106	0.000061	1.007636	0.375000	1.007636	0.0056
45	(44998.3, 45998.24]	8	0.125000	0.000073	1.0	7.0	0.000035	0.000086	0.346476	0.250000	0.661160	0.0056
46	(45998.24, 46998.18]	6	0.333333	0.000055	2.0	4.0	0.000071	0.000049	0.895788	0.208333	0.549312	0.0056
47	(46998.18, 47998.12]	10	0.300000	0.000091	3.0	7.0	0.000106	0.000086	0.807469	0.033333	0.088318	0.0056
48	(47998.12, 48998.06]	5	0.400000	0.000045	2.0	3.0	0.000071	0.000037	1.075805	0.100000	0.268336	0.0056
49	(48998.06, 49998.0]	14	0.214286	0.000127	3.0	11.0	0.000106	0.000135	0.582499	0.185714	0.493306	0.0056

	avg_cur_bal_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(-99.959, 1999.18]	35692	0.246610	0.130586	8802.0	26890.0	0.149990	0.125281	0.787195	NaN	NaN	0.022469
1	(1999.18, 3998.36]	50295	0.251735	0.184014	12661.0	37634.0	0.215749	0.175338	0.802214	0.005125	0.015019	0.022469
2	(3998.36, 5997.54]	30578	0.248970	0.111876	7613.0	22965.0	0.129729	0.106995	0.794114	0.002765	0.008101	0.022469
3	(5997.54, 7996.72]	31977	0.203771	0.116994	6516.0	25461.0	0.111035	0.118624	0.660640	0.045198	0.133473	0.022469
4	(7996.72, 9995.9]	13877	0.224616	0.050772	3117.0	10760.0	0.053115	0.050131	0.722473	0.020845	0.061833	0.022469
5	(9995.9, 11995.08]	12279	0.217037	0.044925	2665.0	9614.0	0.045413	0.044792	0.700053	0.007579	0.022420	0.022469
6	(11995.08, 13994.26]	11237	0.203613	0.041113	2288.0	8949.0	0.038988	0.041694	0.660168	0.013424	0.039885	0.022469
7	(13994.26, 15993.44]	10383	0.192237	0.037988	1996.0	8387.0	0.034013	0.039075	0.626174	0.011376	0.033995	0.022469
8	(15993.44, 17992.62]	9522	0.198173	0.034838	1887.0	7635.0	0.032155	0.035572	0.643934	0.005935	0.017761	0.022469
9	(17992.62, 19991.8]	8585	0.181712	0.031410	1560.0	7025.0	0.026583	0.032730	0.594542	0.016460	0.049393	0.022469
10	(19991.8, 21990.98]	7630	0.190170	0.027916	1451.0	6179.0	0.024726	0.028788	0.619976	0.008458	0.025434	0.022469
11	(21990.98, 23990.16]	6668	0.172016	0.024396	1147.0	5521.0	0.019545	0.025722	0.565231	0.018155	0.054745	0.022469
12	(23990.16, 25989.34]	5819	0.177006	0.021290	1030.0	4789.0	0.017552	0.022312	0.580338	0.004991	0.015107	0.022469
13	(25989.34, 27988.52]	5157	0.173744	0.018868	896.0	4261.0	0.015268	0.019852	0.570469	0.003262	0.009869	0.022469
14	(27988.52, 29987.7]	4416	0.162817	0.016157	719.0	3697.0	0.012252	0.017224	0.537264	0.010927	0.033205	0.022469
15	(29987.7, 31986.88]	3822	0.158817	0.013984	607.0	3215.0	0.010344	0.014979	0.525052	0.004000	0.012213	0.022469
16	(31986.88, 33986.06]	3328	0.156550	0.012176	521.0	2807.0	0.008878	0.013078	0.518115	0.002267	0.006937	0.022469
17	(33986.06, 35985.24]	2845	0.159930	0.010409	455.0	2390.0	0.007753	0.011135	0.528451	0.003379	0.010336	0.022469
18	(35985.24, 37984.42]	2445	0.161963	0.008946	396.0	2049.0	0.006748	0.009546	0.534660	0.002033	0.006209	0.022469
19	(37984.42, 39983.6]	2161	0.160574	0.007906	347.0	1814.0	0.005913	0.008451	0.530419	0.001389	0.004241	0.022469
20	(39983.6, 41982.78]	1800	0.162778	0.006586	293.0	1507.0	0.004993	0.007021	0.537145	0.002204	0.006726	0.022469
21	(41982.78, 43981.96]	1538	0.141743	0.005627	218.0	1320.0	0.003715	0.006150	0.472527	0.021035	0.064618	0.022469
22	(43981.96, 45981.14]	1417	0.143260	0.005184	203.0	1214.0	0.003459	0.005656	0.477223	0.001518	0.004696	0.022469
23	(45981.14, 47980.32]	1246	0.154896	0.004559	193.0	1053.0	0.003289	0.004906	0.513044	0.011635	0.035822	0.022469
24	(47980.32, 49979.5]	1020	0.139216	0.003732	142.0	878.0	0.002420	0.004091	0.464697	0.015680	0.048347	0.022469
25	(49979.5, 51978.68]	919	0.126224	0.003362	116.0	803.0	0.001977	0.003741	0.424193	0.012992	0.040504	0.022469
26	(51978.68, 53977.86]	781	0.134443	0.002857	105.0	676.0	0.001789	0.003150	0.449867	0.008219	0.025674	0.022469
27	(53977.86, 55977.04]	698	0.136103	0.002554	95.0	603.0	0.001619	0.002809	0.455032	0.001660	0.005165	0.022469
28	(55977.04, 57976.22]	594	0.134680	0.002173	80.0	514.0	0.001363	0.002395	0.450605	0.001423	0.004427	0.022469
29	(57976.22, 59975.4]	542	0.134686	0.001983	73.0	469.0	0.001244	0.002185	0.450624	0.000006	0.000019	0.022469
30	(59975.4, 61974.58]	499	0.128257	0.001826	64.0	435.0	0.001091	0.002027	0.430558	0.006430	0.020066	0.022469
31	(61974.58, 63973.76]	441	0.111111	0.001613	49.0	392.0	0.000835	0.001826	0.376509	0.017145	0.054049	0.022469
32	(63973.76, 65972.94]	367	0.128065	0.001343	47.0	320.0	0.000801	0.001491	0.429960	0.016954	0.053451	0.022469
33	(65972.94, 67972.12]	317	0.138801	0.001160	44.0	273.0	0.000750	0.001272	0.463412	0.010736	0.033452	0.022469
34	(67972.12, 69971.3]	281	0.135231	0.001028	38.0	243.0	0.000648	0.001132	0.452320	0.003570	0.011092	0.022469
35	(69971.3, 71970.48]	249	0.136546	0.000911	34.0	215.0	0.000579	0.001002	0.456409	0.001315	0.004089	0.022469
36	(71970.48, 73969.66]	247	0.097166	0.000904	24.0	223.0	0.000409	0.001039	0.331914	0.039380	0.124495	0.022469
37	(73969.66, 75968.84]	219	0.155251	0.000801	34.0	185.0	0.000579	0.000862	0.514134	0.058085	0.182220	0.022469
38	(75968.84, 77968.02]	190	0.110526	0.000695	21.0	169.0	0.000358	0.000787	0.374650	0.044725	0.139484	0.022469
39	(77968.02, 79967.2]	176	0.142045	0.000644	25.0	151.0	0.000426	0.000704	0.473465	0.031519	0.098814	0.022469
40	(79967.2, 81966.38]	180	0.111111	0.000659	20.0	160.0	0.000341	0.000745	0.376509	0.030934	0.096956	0.022469
41	(81966.38, 83965.56]	143	0.090909	0.000523	13.0	130.0	0.000222	0.000606	0.311704	0.020202	0.064805	0.022469
42	(83965.56, 85964.74]	114	0.096491	0.000417	11.0	103.0	0.000187	0.000480	0.329741	0.005582	0.018036	0.022469
43	(85964.74, 87963.92]	128	0.132812	0.000468	17.0	111.0	0.000290	0.000517	0.444787	0.036321	0.115047	0.022469
44	(87963.92, 89963.1]	109	0.110092	0.000399	12.0	97.0	0.000204	0.000452	0.373269	0.022721	0.071518	0.022469
45	(89963.1, 91962.28]	102	0.078431	0.000373	8.0	94.0	0.000136	0.000438	0.271001	0.031660	0.102267	0.022469
46	(91962.28, 93961.46]	92	0.065217	0.000337	6.0	86.0	0.000102	0.000401	0.227275	0.013214	0.043727	0.022469
47	(93961.46, 95960.64]	66	0.151515	0.000241	10.0	56.0	0.000170	0.000261	0.502668	0.086298	0.275393	0.022469
48	(95960.64, 97959.82]	66	0.136364	0.000241	9.0	57.0	0.000153	0.000266	0.455842	0.015152	0.046826	0.022469
49	(97959.82, 99959.0]	64	0.093750	0.000234	6.0	58.0	0.000102	0.000270	0.320896	0.042614	0.134946	0.022469

	bc_open_to_buy_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(-99.997, 1999.94]	79292	0.248865	0.290178	19733.0	59559.0	0.336115	0.277607	0.793336	NaN	NaN	0.020203
1	(1999.94, 3999.88]	40816	0.237505	0.149371	9694.0	31122.0	0.165119	0.145061	0.759999	0.011360	0.033336	0.020203
2	(3999.88, 5999.82]	40135	0.210166	0.146879	8435.0	31700.0	0.143675	0.147755	0.679243	0.027339	0.080757	0.020203
3	(5999.82, 7999.76]	19726	0.215300	0.072190	4247.0	15479.0	0.072340	0.072148	0.694473	0.005134	0.015231	0.020203
4	(7999.76, 9999.7]	14927	0.202184	0.054627	3018.0	11909.0	0.051406	0.055508	0.655495	0.013116	0.038979	0.020203
5	(9999.7, 11999.64]	11763	0.206920	0.043048	2434.0	9329.0	0.041459	0.043483	0.669596	0.004736	0.014101	0.020203
6	(11999.64, 13999.58]	9316	0.197617	0.034093	1841.0	7475.0	0.031358	0.034841	0.641867	0.009303	0.027729	0.020203
7	(13999.58, 15999.52]	7759	0.185591	0.028395	1440.0	6319.0	0.024528	0.029453	0.605829	0.012026	0.036037	0.020203
8	(15999.52, 17999.46]	6360	0.183176	0.023275	1165.0	5195.0	0.019844	0.024214	0.598565	0.002415	0.007264	0.020203
9	(17999.46, 19999.4]	5465	0.185910	0.020000	1016.0	4449.0	0.017306	0.020737	0.606789	0.002734	0.008224	0.020203
10	(19999.4, 21999.34]	4618	0.172152	0.016900	795.0	3823.0	0.013541	0.017819	0.565275	0.013758	0.041514	0.020203
11	(21999.34, 23999.28]	3823	0.171593	0.013991	656.0	3167.0	0.011174	0.014762	0.563580	0.000559	0.001695	0.020203
12	(23999.28, 25999.22]	3527	0.176354	0.012907	622.0	2905.0	0.010595	0.013540	0.577988	0.004761	0.014409	0.020203
13	(25999.22, 27999.16]	2843	0.168484	0.010404	479.0	2364.0	0.008159	0.011019	0.554148	0.007870	0.023841	0.020203
14	(27999.16, 29999.1]	2488	0.162379	0.009105	404.0	2084.0	0.006881	0.009714	0.535573	0.006105	0.018574	0.020203
15	(29999.1, 31999.04]	2306	0.150043	0.008439	346.0	1960.0	0.005893	0.009136	0.497805	0.012336	0.037768	0.020203
16	(31999.04, 33998.98]	1986	0.146022	0.007268	290.0	1696.0	0.004940	0.007905	0.485423	0.004021	0.012383	0.020203
17	(33998.98, 35998.92]	1682	0.140309	0.006155	236.0	1446.0	0.004020	0.006740	0.467766	0.005713	0.017656	0.020203
18	(35998.92, 37998.86]	1521	0.132150	0.005566	201.0	1320.0	0.003424	0.006153	0.442414	0.008159	0.025352	0.020203
19	(37998.86, 39998.8]	1411	0.138909	0.005164	196.0	1215.0	0.003339	0.005663	0.463426	0.006759	0.021012	0.020203
20	(39998.8, 41998.74]	1213	0.136026	0.004439	165.0	1048.0	0.002810	0.004885	0.454479	0.002882	0.008947	0.020203
21	(41998.74, 43998.68]	1061	0.136664	0.003883	145.0	916.0	0.002470	0.004270	0.456459	0.000637	0.001980	0.020203
22	(43998.68, 45998.62]	948	0.165612	0.003469	157.0	791.0	0.002674	0.003687	0.545418	0.028948	0.088959	0.020203
23	(45998.62, 47998.56]	843	0.134045	0.003085	113.0	730.0	0.001925	0.003403	0.448317	0.031567	0.097100	0.020203
24	(47998.56, 49998.5]	726	0.119835	0.002657	87.0	639.0	0.001482	0.002978	0.403825	0.014210	0.044492	0.020203
25	(49998.5, 51998.44]	702	0.136752	0.002569	96.0	606.0	0.001635	0.002825	0.456734	0.016917	0.052909	0.020203
26	(51998.44, 53998.38]	608	0.139803	0.002225	85.0	523.0	0.001448	0.002438	0.466197	0.003050	0.009463	0.020203
27	(53998.38, 55998.32]	547	0.126143	0.002002	69.0	478.0	0.001175	0.002228	0.423641	0.013660	0.042557	0.020203
28	(55998.32, 57998.26]	521	0.099808	0.001907	52.0	469.0	0.000886	0.002186	0.340162	0.026335	0.083479	0.020203
29	(57998.26, 59998.2]	485	0.107216	0.001775	52.0	433.0	0.000886	0.002018	0.363852	0.007408	0.023690	0.020203
30	(59998.2, 61998.14]	404	0.150990	0.001478	61.0	343.0	0.001039	0.001599	0.500715	0.043774	0.136864	0.020203
31	(61998.14, 63998.08]	367	0.100817	0.001343	37.0	330.0	0.000630	0.001538	0.343399	0.050173	0.157316	0.020203
32	(63998.08, 65998.02]	319	0.119122	0.001167	38.0	281.0	0.000647	0.001310	0.401580	0.018305	0.058181	0.020203
33	(65998.02, 67997.96]	307	0.110749	0.001124	34.0	273.0	0.000579	0.001272	0.375090	0.008373	0.026491	0.020203
34	(67997.96, 69997.9]	256	0.117188	0.000937	30.0	226.0	0.000511	0.001053	0.395477	0.006438	0.020387	0.020203
35	(69997.9, 71997.84]	243	0.135802	0.000889	33.0	210.0	0.000562	0.000979	0.453783	0.018615	0.058306	0.020203
36	(71997.84, 73997.78]	246	0.126016	0.000900	31.0	215.0	0.000528	0.001002	0.423245	0.009786	0.030539	0.020203
37	(73997.78, 75997.72]	194	0.092784	0.000710	18.0	176.0	0.000307	0.000820	0.317538	0.033233	0.105707	0.020203
38	(75997.72, 77997.66]	205	0.097561	0.000750	20.0	185.0	0.000341	0.000862	0.332942	0.004777	0.015404	0.020203
39	(77997.66, 79997.6]	183	0.092896	0.000670	17.0	166.0	0.000290	0.000774	0.317902	0.004665	0.015040	0.020203
40	(79997.6, 81997.54]	161	0.136646	0.000589	22.0	139.0	0.000375	0.000648	0.456404	0.043750	0.138502	0.020203
41	(81997.54, 83997.48]	144	0.097222	0.000527	14.0	130.0	0.000238	0.000606	0.331852	0.039424	0.124552	0.020203
42	(83997.48, 85997.42]	140	0.128571	0.000512	18.0	122.0	0.000307	0.000569	0.431242	0.031349	0.099390	0.020203
43	(85997.42, 87997.36]	100	0.060000	0.000366	6.0	94.0	0.000102	0.000438	0.209659	0.068571	0.221583	0.020203
44	(87997.36, 89997.3]	130	0.123077	0.000476	16.0	114.0	0.000273	0.000531	0.414024	0.063077	0.204365	0.020203
45	(89997.3, 91997.24]	111	0.099099	0.000406	11.0	100.0	0.000187	0.000466	0.337885	0.023978	0.076138	0.020203
46	(91997.24, 93997.18]	95	0.105263	0.000348	10.0	85.0	0.000170	0.000396	0.357622	0.006164	0.019737	0.020203
47	(93997.18, 95997.12]	82	0.085366	0.000300	7.0	75.0	0.000119	0.000350	0.293471	0.019897	0.064151	0.020203
48	(95997.12, 97997.06]	73	0.095890	0.000267	7.0	66.0	0.000119	0.000308	0.327564	0.010525	0.034093	0.020203
49	(97997.06, 99997.0]	75	0.133333	0.000274	10.0	65.0	0.000170	0.000303	0.446101	0.037443	0.118537	0.020203

	revol_bal_to_bc_limit_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(-0.957, 19.135]	270872	0.214216	0.998231	58025.0	212847.0	0.998125	0.998260	0.693080	NaN	NaN	0.00009
1	(19.135, 38.269]	354	0.225989	0.001305	80.0	274.0	0.001376	0.001285	0.727964	0.011773	0.034885	0.00009
2	(38.269, 57.404]	65	0.292308	0.000240	19.0	46.0	0.000327	0.000216	0.922241	0.066319	0.194276	0.00009
3	(57.404, 76.538]	25	0.240000	0.000092	6.0	19.0	0.000103	0.000089	0.769284	0.052308	0.152957	0.00009
4	(76.538, 95.673]	13	0.153846	0.000048	2.0	11.0	0.000034	0.000052	0.510938	0.086154	0.258346	0.00009
5	(95.673, 114.807]	8	0.125000	0.000029	1.0	7.0	0.000017	0.000033	0.421310	0.028846	0.089628	0.00009
6	(114.807, 133.942]	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	0.125000	0.421310	0.00009
7	(133.942, 153.076]	3	0.000000	0.000011	0.0	3.0	0.000000	0.000014	0.000000	0.000000	0.000000	0.00009
8	(153.076, 172.211]	2	0.500000	0.000007	1.0	1.0	0.000017	0.000005	1.540666	0.500000	1.540666	0.00009
9	(172.211, 191.345]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.500000	1.540666	0.00009
10	(191.345, 210.48]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.000000	0.000000	0.00009
11	(210.48, 229.614]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.000000	0.000000	0.00009
12	(229.614, 248.749]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.000000	0.000000	0.00009
13	(248.749, 267.883]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
14	(267.883, 287.018]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
15	(287.018, 306.153]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	0.00009
16	(306.153, 325.287]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
17	(325.287, 344.422]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
18	(344.422, 363.556]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
19	(363.556, 382.691]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
20	(382.691, 401.825]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
21	(401.825, 420.96]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
22	(420.96, 440.094]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	0.00009
23	(440.094, 459.229]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
24	(459.229, 478.363]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
25	(478.363, 497.498]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
26	(497.498, 516.632]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
27	(516.632, 535.767]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
28	(535.767, 554.901]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
29	(554.901, 574.036]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
30	(574.036, 593.171]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
31	(593.171, 612.305]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
32	(612.305, 631.44]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
33	(631.44, 650.574]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
34	(650.574, 669.709]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
35	(669.709, 688.843]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	0.00009
36	(688.843, 707.978]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
37	(707.978, 727.112]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
38	(727.112, 746.247]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
39	(746.247, 765.381]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
40	(765.381, 784.516]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
41	(784.516, 803.65]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
42	(803.65, 822.785]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
43	(822.785, 841.919]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
44	(841.919, 861.054]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
45	(861.054, 880.189]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
46	(880.189, 899.323]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
47	(899.323, 918.458]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
48	(918.458, 937.592]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00009
49	(937.592, 956.727]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	0.00009

	revol_bal_to_bc_limit_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(-0.01, 0.2]	20300	0.158276	0.075207	3213.0	17087.0	0.055614	0.080543	0.525020	NaN	NaN	0.0144
1	(0.2, 0.4]	30809	0.173910	0.114141	5358.0	25451.0	0.092742	0.119968	0.572706	0.015634	0.047686	0.0144
2	(0.4, 0.6]	40014	0.197531	0.148243	7904.0	32110.0	0.136811	0.151357	0.643905	0.023621	0.071199	0.0144
3	(0.6, 0.8]	44351	0.212126	0.164311	9408.0	34943.0	0.162844	0.164710	0.687466	0.014595	0.043561	0.0144
4	(0.8, 1.0]	48756	0.224342	0.180631	10938.0	37818.0	0.189327	0.178262	0.723711	0.012216	0.036245	0.0144
5	(1.0, 1.2]	28863	0.241694	0.106931	6976.0	21887.0	0.120748	0.103169	0.774911	0.017352	0.051201	0.0144
6	(1.2, 1.4]	15821	0.246950	0.058613	3907.0	11914.0	0.067627	0.056159	0.790366	0.005257	0.015455	0.0144
7	(1.4, 1.6]	9904	0.247274	0.036692	2449.0	7455.0	0.042390	0.035141	0.791317	0.000324	0.000951	0.0144
8	(1.6, 1.8]	6701	0.255932	0.024826	1715.0	4986.0	0.029685	0.023502	0.816720	0.008658	0.025404	0.0144
9	(1.8, 2.0]	4674	0.241335	0.017316	1128.0	3546.0	0.019525	0.016715	0.773857	0.014597	0.042864	0.0144
10	(2.0, 2.2]	3362	0.237359	0.012455	798.0	2564.0	0.013813	0.012086	0.762149	0.003976	0.011708	0.0144
11	(2.2, 2.4]	2546	0.260408	0.009432	663.0	1883.0	0.011476	0.008876	0.829833	0.023050	0.067685	0.0144
12	(2.4, 2.6]	1939	0.242909	0.007184	471.0	1468.0	0.008153	0.006920	0.778486	0.017500	0.051347	0.0144
13	(2.6, 2.8]	1580	0.235443	0.005854	372.0	1208.0	0.006439	0.005694	0.756503	0.007466	0.021984	0.0144
14	(2.8, 3.0]	1329	0.253574	0.004924	337.0	992.0	0.005833	0.004676	0.809808	0.018131	0.053305	0.0144
15	(3.0, 3.2]	1068	0.230337	0.003957	246.0	822.0	0.004258	0.003875	0.741436	0.023237	0.068371	0.0144
16	(3.2, 3.4]	903	0.241417	0.003345	218.0	685.0	0.003773	0.003229	0.774099	0.011080	0.032663	0.0144
17	(3.4, 3.6]	781	0.247119	0.002893	193.0	588.0	0.003341	0.002772	0.790862	0.005702	0.016763	0.0144
18	(3.6, 3.8]	670	0.232836	0.002482	156.0	514.0	0.002700	0.002423	0.748813	0.014283	0.042049	0.0144
19	(3.8, 4.0]	545	0.260550	0.002019	142.0	403.0	0.002458	0.001900	0.830249	0.027715	0.081436	0.0144
20	(4.0, 4.2]	491	0.254582	0.001819	125.0	366.0	0.002164	0.001725	0.812765	0.005968	0.017484	0.0144
21	(4.2, 4.4]	442	0.212670	0.001638	94.0	348.0	0.001627	0.001640	0.689083	0.041913	0.123682	0.0144
22	(4.4, 4.6]	416	0.194712	0.001541	81.0	335.0	0.001402	0.001579	0.635454	0.017958	0.053628	0.0144
23	(4.6, 4.8]	363	0.258953	0.001345	94.0	269.0	0.001627	0.001268	0.825572	0.064242	0.190117	0.0144
24	(4.8, 5.0]	285	0.266667	0.001056	76.0	209.0	0.001315	0.000985	0.848144	0.007713	0.022572	0.0144
25	(5.0, 5.2]	262	0.217557	0.000971	57.0	205.0	0.000987	0.000966	0.703603	0.049109	0.144540	0.0144
26	(5.2, 5.4]	237	0.164557	0.000878	39.0	198.0	0.000675	0.000933	0.544236	0.053000	0.159367	0.0144
27	(5.4, 5.6]	239	0.271967	0.000885	65.0	174.0	0.001125	0.000820	0.863632	0.107410	0.319396	0.0144
28	(5.6, 5.8]	201	0.179104	0.000745	36.0	165.0	0.000623	0.000778	0.588445	0.092862	0.275188	0.0144
29	(5.8, 6.0]	201	0.253731	0.000745	51.0	150.0	0.000883	0.000707	0.810269	0.074627	0.221824	0.0144
30	(6.0, 6.2]	163	0.251534	0.000604	41.0	122.0	0.000710	0.000575	0.803823	0.002198	0.006446	0.0144
31	(6.2, 6.4]	146	0.321918	0.000541	47.0	99.0	0.000814	0.000467	1.009168	0.070384	0.205345	0.0144
32	(6.4, 6.599]	143	0.265734	0.000530	38.0	105.0	0.000658	0.000495	0.845417	0.056184	0.163751	0.0144
33	(6.599, 6.799]	146	0.273973	0.000541	40.0	106.0	0.000692	0.000500	0.869491	0.008238	0.024074	0.0144
34	(6.799, 6.999]	125	0.200000	0.000463	25.0	100.0	0.000433	0.000471	0.651295	0.073973	0.218196	0.0144
35	(6.999, 7.199]	134	0.276119	0.000496	37.0	97.0	0.000640	0.000457	0.875759	0.076119	0.224463	0.0144
36	(7.199, 7.399]	120	0.200000	0.000445	24.0	96.0	0.000415	0.000453	0.651295	0.076119	0.224463	0.0144
37	(7.399, 7.599]	84	0.297619	0.000311	25.0	59.0	0.000433	0.000278	0.938433	0.097619	0.287137	0.0144
38	(7.599, 7.799]	104	0.230769	0.000385	24.0	80.0	0.000415	0.000377	0.742713	0.066850	0.195720	0.0144
39	(7.799, 7.999]	106	0.216981	0.000393	23.0	83.0	0.000398	0.000391	0.701893	0.013788	0.040819	0.0144
40	(7.999, 8.199]	80	0.212500	0.000296	17.0	63.0	0.000294	0.000297	0.688578	0.004481	0.013315	0.0144
41	(8.199, 8.399]	71	0.295775	0.000263	21.0	50.0	0.000363	0.000236	0.933061	0.083275	0.244483	0.0144
42	(8.399, 8.599]	63	0.190476	0.000233	12.0	51.0	0.000208	0.000240	0.622737	0.105298	0.310325	0.0144
43	(8.599, 8.799]	67	0.238806	0.000248	16.0	51.0	0.000277	0.000240	0.766412	0.048330	0.143675	0.0144
44	(8.799, 8.999]	56	0.214286	0.000207	12.0	44.0	0.000208	0.000207	0.693887	0.024520	0.072524	0.0144
45	(8.999, 9.199]	72	0.194444	0.000267	14.0	58.0	0.000242	0.000273	0.634653	0.019841	0.059234	0.0144
46	(9.199, 9.399]	48	0.333333	0.000178	16.0	32.0	0.000277	0.000151	1.042412	0.138889	0.407758	0.0144
47	(9.399, 9.599]	55	0.254545	0.000204	14.0	41.0	0.000242	0.000193	0.812656	0.078788	0.229756	0.0144
48	(9.599, 9.799]	43	0.209302	0.000159	9.0	34.0	0.000156	0.000160	0.679061	0.045243	0.133595	0.0144
49	(9.799, 9.999]	42	0.190476	0.000156	8.0	34.0	0.000138	0.000160	0.622737	0.018826	0.056324	0.0144

	revol_bal_to_open_to_buy_factor	n_obs	prop_good	prop_n_obs	n_good	n_bad	prop_n_good	prop_n_bad	WoE	diff_prop_good	diff_WoE	IV
0	(-36.021, 720.42]	269193	0.213092	0.997410	57363.0	211830.0	0.996681	0.997608	0.692683	NaN	NaN	inf
1	(720.42, 1440.84]	377	0.262599	0.001397	99.0	278.0	0.001720	0.001309	0.838909	0.049507	0.146226	inf
2	(1440.84, 2161.26]	120	0.308333	0.000445	37.0	83.0	0.000643	0.000391	0.972542	0.045734	0.133633	inf
3	(2161.26, 2881.68]	53	0.320755	0.000196	17.0	36.0	0.000295	0.000170	1.008761	0.012421	0.036219	inf
4	(2881.68, 3602.1]	35	0.200000	0.000130	7.0	28.0	0.000122	0.000132	0.653544	0.120755	0.355217	inf
5	(3602.1, 4322.52]	25	0.280000	0.000093	7.0	18.0	0.000122	0.000085	0.889846	0.080000	0.236302	inf
6	(4322.52, 5042.94]	20	0.250000	0.000074	5.0	15.0	0.000087	0.000071	0.801907	0.030000	0.087939	inf
7	(5042.94, 5763.36]	13	0.384615	0.000048	5.0	8.0	0.000087	0.000038	1.195696	0.134615	0.393788	inf
8	(5763.36, 6483.78]	7	0.142857	0.000026	1.0	6.0	0.000017	0.000028	0.479270	0.241758	0.716426	inf
9	(6483.78, 7204.2]	6	0.166667	0.000022	1.0	5.0	0.000017	0.000024	0.552663	0.023810	0.073393	inf
10	(7204.2, 7924.62]	7	0.285714	0.000026	2.0	5.0	0.000035	0.000024	0.906543	0.119048	0.353880	inf
11	(7924.62, 8645.04]	5	0.400000	0.000019	2.0	3.0	0.000035	0.000014	1.241147	0.114286	0.334605	inf
12	(8645.04, 9365.46]	5	0.000000	0.000019	0.0	5.0	0.000000	0.000024	0.000000	0.400000	1.241147	inf
13	(9365.46, 10085.88]	4	0.500000	0.000015	2.0	2.0	0.000035	0.000009	1.545298	0.500000	1.545298	inf
14	(10085.88, 10806.3]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.500000	1.545298	inf
15	(10806.3, 11526.72]	3	0.333333	0.000011	1.0	2.0	0.000017	0.000009	1.045452	0.333333	1.045452	inf
16	(11526.72, 12247.14]	2	0.500000	0.000007	1.0	1.0	0.000017	0.000005	1.545298	0.166667	0.499846	inf
17	(12247.14, 12967.56]	5	0.200000	0.000019	1.0	4.0	0.000017	0.000019	0.653544	0.300000	0.891754	inf
18	(12967.56, 13687.98]	2	0.000000	0.000007	0.0	2.0	0.000000	0.000009	0.000000	0.200000	0.653544	inf
19	(13687.98, 14408.4]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
20	(14408.4, 15128.82]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	inf
21	(15128.82, 15849.24]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.000000	0.000000	inf
22	(15849.24, 16569.66]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.000000	0.000000	inf
23	(16569.66, 17290.08]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	0.000000	0.000000	inf
24	(17290.08, 18010.5]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
25	(18010.5, 18730.92]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
26	(18730.92, 19451.34]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
27	(19451.34, 20171.76]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
28	(20171.76, 20892.18]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
29	(20892.18, 21612.6]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
30	(21612.6, 22333.02]	1	0.000000	0.000004	0.0	1.0	0.000000	0.000005	0.000000	NaN	NaN	inf
31	(22333.02, 23053.44]	1	1.000000	0.000004	1.0	0.0	0.000017	0.000000	inf	1.000000	inf	inf
32	(23053.44, 23773.86]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
33	(23773.86, 24494.28]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
34	(24494.28, 25214.7]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
35	(25214.7, 25935.12]	1	1.000000	0.000004	1.0	0.0	0.000017	0.000000	inf	NaN	NaN	inf
36	(25935.12, 26655.54]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
37	(26655.54, 27375.96]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
38	(27375.96, 28096.38]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
39	(28096.38, 28816.8]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
40	(28816.8, 29537.22]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
41	(29537.22, 30257.64]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
42	(30257.64, 30978.06]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
43	(30978.06, 31698.48]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
44	(31698.48, 32418.9]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
45	(32418.9, 33139.32]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
46	(33139.32, 33859.74]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
47	(33859.74, 34580.16]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
48	(34580.16, 35300.58]	0	NaN	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf
49	(35300.58, 36021.0]	2	0.500000	0.000007	1.0	1.0	0.000017	0.000005	1.545298	NaN	NaN	inf